arXiv Daily Digest - 2026-05-20

Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a \emph{streaming-first} principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based ring-buffer renderer enables interactive browsing and visualization of arbitrarily long recordings with constant memory usage. An integrated annotation interface supports event labeling directly within continuous data streams, facilitating the creation of reproducible machine-learning-ready labeled datasets without offline preprocessing. The signal processing library includes temporal, spatial, spectral, and decomposition-based operators, with both CPU implementations and GPU-accelerated variants via PyTorch, alongside stateful chunked execution that preserves processing continuity and application semantics across segment boundaries. A standardized monitor interface further integrates streaming detectors and learning-based models into the visualization workflow. By sharing a common streaming abstraction across all layers, FiLark allows processing configurations and workflows developed interactively to transfer directly to scalable production pipelines without modification.

This paper proposes CAT-MoEformer, a context-aware transformer with scene-conditioned mixture-of-experts (MoE) feed-forward networks, for proactive mmWave beam prediction from compressed uplink pilot observations. The spatial encoder comprises a three-layer asymmetric convolutional network followed by a squeeze-and-excitation recalibration block, which extracts frequency-beam correlation features from pilot tensors without explicit channel reconstruction. A truncated pretrained GPT-2 backbone models the temporal evolution of beam sequences, with the feed-forward networks in the upper three transformer layers replaced by scene-conditioned MoE-FFN modules. A lightweight gating network maps the scenario label and normalized user equipment speed to expert mixing weights, conditioning the routing decision on physical propagation descriptors rather than on latent hidden states. This design yields interpretable expert assignments and eliminates the load imbalance associated with token-level routing. To prevent expert collapse under soft routing, a three-stage training strategy is introduced: hard expert assignment in the first stage establishes scene-specific specialization, isolated gating network training in the second stage aligns the soft routing distribution with the hard partition, and top-1 hard inference in the third stage fine-tunes the model under deterministic single-expert activation to maximize scene-specific precision. Simulation results on 3GPP TR 38.901 Urban Macro channel simulations with $64{,}000$ user samples demonstrate that CAT-MoEformer achieves a Top-1 beam prediction accuracy of $94.88\%$ and a beam switching instant accuracy of $80.62\%$, representing gains of $2.33\%$ and $9.55\%$ respectively over a CNN+GPT-2 baseline, with an inference latency of $0.52$~ms.

Blind source separation (BSS) is a natural framework for studying how latent causes may be recovered from sensory mixtures, but deriving online and biologically plausible algorithms for structured (i.e., constrained to known domains) and potentially correlated sources remains challenging. Recent work has derived neural networks for BSS from maximization of an entropy measure, yet its online implementations involve complex and nonlocal recurrent dynamics. Motivated by this perspective, we propose Predictive Entropy Maximization, which achieves competitive performance in BSS, using only local weight updates. The method employs a close approximation of an entropy measure, yielding an objective function with easily interpretable components. Minimizing this objective leads to a predictive neural architecture in which feedforward synapses follow an error-driven rule (that can be realized through dendritic mechanisms), lateral inhibitory connections are learned with local Hebbian plasticity, and source-domain constraints are enforced through simple output nonlinearities. We derive explicit spectral bounds on the surrogate error, characterizing when the approximation is accurate. Empirically, Predictive Entropy Maximization remains robust under increasing source correlation and observation noise, outperforms biologically plausible algorithms that rely on stronger independence or decorrelation assumptions, and remains competitive with exact determinant- and correlative-information-based baselines. These results show how local plasticity and adaptive lateral inhibition can emerge from maximizing a regularized second-order entropy over structured source domains. Our implementation code is available at https://github.com/BariscanBozkurt/Predictive-Entropy-Maximization.

This paper addresses the problem of tracking in-plane waves from image sequences using periodic surface patterns. Wave-induced deformation is modeled as a spatial phase modulation of a periodic carrier. We propose ADOPT (Analytical Demodulation of Periodic Texture), a method based on an oriented two-dimensional analytic signal to estimate displacement phase and orientation. The approach relies on a physical model describing longitudinal and transverse in-plane waves. Orientation-selective filtering isolates relevant spectral components, and phase extraction provides a stable reconstruction of the displacement field. A theoretical analysis using the Cramer--Rao bound evaluates performance limits of ADOPT. Simulations show that the proposed method outperforms state-of-the-art Digital Image Correlation (DIC) at high signal-to-noise ratios, especially for small displacements where DIC becomes limited. Moreover, ADOPT is more computationally efficient. Experiments on silicone membranes with periodic patterns confirm accurate estimation of wave fields and dispersion curves under impulsive excitation. Overall, the proposed framework provides a robust and efficient solution for wave-induced displacement estimation.

Beyond diagonal reconfigurable intelligent surface (BD-RIS) represents a promising architecture for advancing millimeter-wave (mmWave) communications. However, its intricate inter-element connections invalidate the conventional decoupled mathematical structure, thereby severely complicating cascaded channel estimation. In this paper, we formulate a novel block-Kronecker-structured cascaded channel model for a \textit{group-connected} BD-RIS-aided multi-user (MU) mmWave system equipped with uniform planar arrays (UPAs). By exploiting the cascaded channel sparsity, an efficient three-stage estimation protocol is proposed. Specifically, Stage I acquires the common angles of arrival (AoAs) at the base station (BS) via a discrete Fourier transform (DFT)-based approach. Stage II leverages the block-Kronecker structure alongside orthogonal matching pursuit (OMP) and correlation-based least squares (LS) to extract the complete cascaded channel for a designated typical user. Finally, Stage III utilizes a Hierarchical Block OMP (HBOMP) algorithm to estimate the other users' channels. This structurally reconstructs the common and user-specific components, which fundamentally reduces the computational complexity and substantially reduces the pilot overhead. Numerical simulations verify that the proposed protocol yields improved channel estimation accuracy while maintaining a relatively low pilot overhead.

In this paper, we theoretically analyze and experimentally demonstrate the performance gains achievable by integrating an in-house built reconfigurable intelligent surface (RIS) with a 5G new radio (NR) system implemented using the OpenAirInterface (OAI) software stack. Unlike conventional RIS-assisted systems that rely on explicit channel state information (CSI) estimation followed by RIS phase configuration optimization, we adopt a low-complexity approach in which the RIS phase states are randomly switched among predefined configurations. The resulting channel fluctuations are opportunistically exploited by the inherent proportional fair (PF) scheduling mechanism of 5G NR. We develop a theoretical framework that characterizes the interaction between RIS switching dynamics and PF scheduling. Based on this framework and the associated analysis, we provide design guidelines for selecting the RIS switching time $T_s$ and the PF throughput averaging window $T_c$ that maximize the system throughput. Experimental evaluations on the 5G NR testbed demonstrate improvements in key performance metrics, including reference signal received power (RSRP), block error rate (BLER), modulation and coding scheme (MCS) index, and throughput. Our key takeaway is that randomly configured RIS operation with appropriately chosen system parameters can achieve performance comparable to optimized RIS designs, with no additional overhead compared to a conventional 5G NR system. More importantly, it requires no coordination between the RIS and the 5G NR system.

Discrete affine Fourier transform spread affine frequency division multiplexing (DAFT-s-AFDM) is a promising waveform for integrated sensing and communication (ISAC) due to its low peak-to-average power ratio, robustness to Doppler shifts, and reduced multiuser interference in the uplink transmission. This paper presents a comprehensive ambiguity function (AF) analysis of DAFT-s-AFDM and derives the closed-form expression for the AF magnitude expectation. Several key insights into the impact of DAFT-s-AFDM parameters on ISAC performance are revealed, thus providing concrete guidance for the subsequent waveform design. Building on these insights, a novel probabilistic constellation shaping (PCS) framework is proposed for ISAC waveform enhancement, where the communication throughput and the sensing AF characteristics are jointly optimized by addressing a multi-objective problem. An efficient algorithm based on a closed-form bit error rate expression is developed to obtain the Pareto-optimal solutions. Extensive simulations validate the theoretical results and that the proposed PCS-enhanced DAFT-s-AFDM can significantly outperform the classical counterparts, achieving a superior and highly controllable tradeoff between the dual-functional performances.

In a fluid antenna system (FAS), a single reconfigurable antenna is able to activate one of $N$ correlated ports to exploit spatial diversity. However, outage analysis is challenging because exact evaluation requires an $N$-dimensional multivariate integral, while existing closed-form approximations based on block-correlation models tend to underestimate the true outage probability. This paper shows that the spatial correlation matrix of a FAS with a normalized linear aperture length $W$ has at most $K^{*}=2\lceil W\rceil+1$ significant eigenmodes, regardless of the number of deployed ports. This is a spatial counterpart of the Slepian-Landau-Pollak spectral concentration theorem and reveals that the spatial degrees of freedom are determined by aperture size rather than port count. Motivated by this result, we derive an \emph{equivalent degree of freedom} (EDoF) approximation, under which the outage probability can be expressed in closed form as that of selection combining over $K^{*}$ independent branches. We propose a refined \emph{weighted independent modes} (WIM) approximation, to incorporate eigenvalue-dependent branch weights $\{β_k\}$ and yield a product-form closed-form expression with improved accuracy at moderate signal-to-noise ratio (SNR). Both approximations achieve the exact diversity order, become asymptotically exact at high SNR, and provably never underestimate the true outage probability by Anderson's inequality. The proposed framework is further extended to obtain closed-form expressions for ergodic capacity, characterize multi-user fluid antenna multiple access (FAMA) with explicit interference-limited outage floors. Besides, we analyze two-dimensional planar FAS, for which the diversity order scales multiplicatively with the aperture dimensions.

Numerous multicarrier modulation schemes have been proposed recently to enhance the performance in narrowband doubly dispersive channels for emerging high-mobility applications. However, the ultra-reliable modulation framework in wideband linear time-varying (LTV) channels remains an open problem, where the time dilations and contractions brought by the high mobility cannot be ignored for the baseband signal to obtain the constant Doppler shift across the whole transmission band. To solve this problem, we propose the hyperbolic frequency multicarrier (HFMC) waveform in this paper based on the inspiration from affine frequency division multiplexing (AFDM) modulation, where the delay and Doppler shift are absorbed into a 1D shift in the affine domain to provide a compact characterization of doubly dispersive discrete-time channels. By adopting the passband representation of wideband LTV channels and hyperbolic frequency modulated (HFM) signals, we reveal that the Doppler scaling factor brought by the relative mobility can be absorbed into an equivalent delay. The basic principle of HFMC modulation is established by investigating the approximate orthogonality among HFMC subcarriers, which are generated from a basic HFM signal by utilizing uniformly spaced equivalent delay. The spectrum of HFMC subcarriers is also analyzed to evaluate the system capacity, where the overlapping nature in the frequency domain can be observed. The input-output characterization in wideband LTV channels is then executed to confirm the 1D integration of time delay and Doppler scaling factor for each path, which demonstrates the ability to exploit potential multipath diversity. The parameter optimization based on the input-output relation and spectrum analysis is finally developed to balance the efficiency and reliability.

Driven by the ultra-high throughput requirements of 6G, wireless communications are migrating to centimeter wave (cmWave) bands to overcome the limitations of current spectral resources. Massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) systems aim to achieve high spectral efficiency in cmWave regimes but are often constrained by the heavy overhead of downlink channel state information (CSI) feedback. This paper proposes a deep learning scheme based on the multi-axis multi-layer perceptron for image processing (MAXIM) architecture for joint semantic CSI feedback and hybrid beamforming in multi-user cmWave MIMO-OFDM systems, which maximizes the downlink sum rate by end-to-end optimization. Specifically, distributed encoders at multiple user equipments (UEs) perform limited CSI feedback, while the decoder at the base station (BS) jointly designs the hybrid beamforming matrices without explicit CSI reconstruction. The uplink transmission is implemented via deep joint source-channel coding (DJSCC) to enhance CSI compression efficiency and noise robustness. Furthermore, considering the high correlation between vertical and horizontal polarization channels in dual-polarized massive MIMO systems, a cross-polarization interaction module is introduced at the UEs to exploit polarization correlations for joint CSI compression. Simulation results demonstrate that the proposed method improves the downlink sum rate under various signal-to-noise ratio (SNR) conditions with a limited number of feedback symbols, validating its robustness and superiority in multi-user dual-polarized cmWave MIMO-OFDM systems.

Fluid antenna system (FAS), which continuously repositions a single physical element across a deployment region $[0, D]$, breaks this limit by freeing antenna positions from the discrete grid entirely. This paper establishes the theoretical foundations of sparse FAS design for direction-of-arrival (DOA) estimation and shows that continuous position freedom unlocks three compounding advantages over the classical designs. \emph{First}, we derive a universal dual DOF bound and prove that FAS-optimized positions can approach it, growing the DOF linearly with $D/λ$ , where $λ$ is the signal wavelength, rather than saturating at $O(N^2)$. \emph{Second}, the CRB scales as $O(1/D^{2L})$ for $L$ sources, a $(D/(N^2 d_0))^{2L}$ improvement over the best grid design, with $d_0 = λ/2$ and D-optimal positions admitting closed-form solution for single sources and efficient Frank-Wolfe algorithm for multiple sources. \emph{Third}, we propose a two-stage FAS-MUSIC approach that combines coarray MUSIC disambiguation with full-aperture local maximum likelihood (ML) refinement to track the CRB, overcoming the grating-lobe ambiguity inherent in large-aperture non-uniform arrays. Robustness to minimum spacing constraints, mutual coupling, and finite position accuracy is also analyzed. Extensive simulations show that FAS-MUSIC achieves $17.5\times$ lower root mean squared error (RMSE) than uniform linear array (ULA) MUSIC and that FAS with $4$ antennas outperforms MRA with $8$ antennas, gains that are unattainable by any grid-constrained design.

Time-based indoor positioning techniques rely on multiple access points (APs) and measurements between the user equipment (UE) and the APs. In dense indoor environments, occlusion-induced non-line-of-sight (NLoS) propagation introduces significant delays in these measurements, thereby degrading position estimation accuracy. To address this challenge, this paper proposes measurement selection strategies to improve position estimation accuracy. A ray-tracing (RT) simulator is employed to characterize the propagation environment and derive AP neighborhood information, which is subsequently used to design and evaluate different measurement selection strategies. The approaches explored include AP neighborhood-based cardinality selection, intersection and union of measurements from AP neighborhoods, and fixed measurement selection. Experiments demonstrate the efficacy of the proposed measurement selection strategies in environments under significant NLoS conditions.

In this paper, we propose a quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (MIMO) systems in a random access channel, where numerous user terminals share the same channel resource and asynchronously transmit signals. Classical linear detectors suffer from significant performance degradation in this scenario, whereas the exhaustive-search MLD achieves the optimal performance but incurs an exponential computational complexity. To overcome this trade-off, we formulate the MLD as a binary optimization problem and solve it via Grover adaptive search (GAS) -- a quantum exhaustive search algorithm offering quadratic speedup in fault-tolerant quantum computing. We then introduce a search space reduction technique to substantially decrease the required computational resources. In addition, we investigate efficient parameter settings for GAS through probability analysis to improve convergence performance. We demonstrate that the proposed detector achieves the optimal detection performance while reducing the required Grover rotation count to reach the solution by up to approximately 65% compared with the conventional GAS, showing its potential as a viable solution for future quantum-accelerated wireless systems.

Accurate channel modeling is fundamental to design and evaluation of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. However, existing model-based approaches typically rely on simplified assumptions, such as sparsity or predefined parametric structures, which are insufficient to capture the complex spatial variations and cross far-/near-field propagation characteristics of practical THz channels. In this paper, a conditional diffusion transformer (CDiT) framework is proposed for high-fidelity THz channel generation. By leveraging the state-of-the-art hybrid planar-spherical wave model (HPSM), THz channel modeling is formulated as a geometry-aware conditional generative learning problem in the sparse beamspace domain. Position information is incorporated as a conditioning signal within a diffusion-transformer architecture, enabling effective learning of the spatially dependent channel distribution. By combining the strong distribution modeling capability of diffusion models with the global dependency modeling strength of transformers, the proposed framework achieves controllable and high-fidelity THz channel synthesis. Extensive experiments on realistic THz channel datasets demonstrate that the proposed framework converges stably and significantly outperforms representative benchmark methods. The proposed framework provides a promising data-driven paradigm for THz channel modeling in next-generation wireless systems.

The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based algorithms, even in domains governed by well-understood physical laws. While data-driven models, such as long short-term memory (LSTM) networks, have become a popular choice for time-series analysis, their performance relative to model-based approaches in structured environments is rarely evaluated objectively. This paper presents a performance evaluation framework comparing an LSTM classifier against a model-based expectation maximization (EM) classifier for binary time-series classification. The evaluation is conducted on two scalar linear Gaussian state space models differing only in their noise statistics, where the Kalman filter likelihood ratio test with true parameters serves as a reference for the best achievable classification performance.Through Monte Carlo simulations, the classifiers are evaluated across three axes: task difficulty, controlled by the separation in process or measurement noise between the two models; sequence length; and training dataset size. The results show that the EM classifier, which exploits the known model structure, performs strongly when the data conform to the assumed model class. The LSTM classifier requires a larger separation in noise statistics to achieve reliable classification, and its performance saturates below the reference classifier when the models differ only in measurement noise, regardless of sequence length or training dataset size.

Green hydrogen plays an essential role in decarbonization, with capacity projected to scale to 560 GW by 2030 (vs. 1.39 GW in 2023) in net-zero settings. Proton exchange membrane (PEM) electrolysis is one of the most promising technology routes to green hydrogen production, and real-time system health monitoring of PEM electrolyzers is essential for their scalable deployment. In lab settings, performance degradation can be characterized through electrochemical testing protocols by periodic pauses of normal operation. Such interruption is not practical for full-scale stack deployments, limiting system operators' ability to make real-time assessments of state-of-health (SoH). We present a machine learning (ML) framework that performs virtual electrochemical characterization during normal operation. The method uses an encoder-decoder transformer, conditioned on operational data, to reconstruct characterization outputs, focusing here on polarization curves. Inspired by patch-based sequence tokenization, we segment the inputs into patches and encode them to form meaningful tokens, which substantially improves learning efficiency. Across four longitudinal runs, lasting up to 478 hours on different test cells and loading cycles, the model accurately reconstructed polarization curves and achieved 10x reduction in mean squared error (MSE) compared to a vanilla transformer. This proof-of-concept demonstrates that ML models can enable continuous performance monitoring for PEM electrolyzers and that the encoder captures meaningful latent representations of SoH, opening up opportunities to derive interpretable indicators in future work.

Radars are susceptible to interference from transmissions by other radars, leading to potential issues such as false target generation and masking of true targets. Currently, automotive radars are installed on a small percentage of vehicles, with interference managed under the assumption of infrequent occurrences. However, as radar adoption grows, this assumption will no longer hold, leading to increased severity and likelihood of interference. This paper analyzes the impact of interference in various future scenarios characterized by higher radar density on vehicles and a greater number of radars per vehicle. Conventional interference mitigation techniques are evaluated using a realistic radar processing flow simulation at the IF (Intermediate Frequency) frequency level, incorporating analytical interference modeling. To validate the simulation and assess radar performance under heavy interference conditions, experimental tests were conducted with host radars in environments with to up to 30 interference radars.

This paper proposes a channel estimation method for Multiple-Input Multiple-Output (MIMO) systems based on Canonical Polyadic (CP) decomposition applied to a mode-factorized tensor representation of the channel. The proposed approach reshapes the original low-order channel tensor into a higher-order tensor by factorizing its modes into multiple virtual modes, thereby introducing additional dimensions. By exploiting the sparse structure of MIMO channels and the plane-wave propagation model in the far-field regime, the proposed mode tensorization enhances the separability of individual propagation paths. It is shown that increasing the number of tensor modes improves component separation and provides inherent denoising effects. Building on these properties, a mode-tensorized CP decomposition (MTCPD) algorithm is developed. In addition, a metric for analyzing the virtual factors obtained from MTCPD is proposed, enabling estimation of the canonical rank and selection of the most informative components contributing to overall system performance. Numerical results demonstrate that the proposed method improves channel estimation accuracy compared to conventional tensor-based approaches, particularly under low signal-to-noise ratio conditions.

Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets. In contrast, conventional multi-layer perceptrons (MLPs) are far more tolerant to noise and computationally efficient. Replacing all MLP components with KANs in HAR models often degrades accuracy and computation efficiency, highlighting an open challenge: how to combine KANs' precision with MLPs' noise robustness and efficiency. To address this, we systematically explore various placements of KAN modules within deep HAR networks and propose a hybrid architecture that strategically synergizes the strengths of both paradigms, which uses a KAN-based input embedding layer, retains MLP layers for intermediate feature mixing, and introduces a specialized LarctanKAN module for final activity classification. Across eight public HAR datasets, the hybrid KAN-MLP model achieves an average macro F1 score relative improvement of 5.33\% compared pure-MLP model, significantly outperforming standalone KAN and MLP baselines. Furthermore, integrating this hybrid strategy into other state-of-the-art HAR architectures consistently boosts their performance. Our findings demonstrate that a carefully orchestrated combination of KAN, MLP, or other conventional neural components yields more robust and accurate HAR models for real-world wearable sensing environments.

Dynamic MRI reconstruction from undersampled measurements is a challenging inverse problem that requires preserving both spatial reconstruction quality and temporal consistency across the frames of the cine series. While recent learning-based approaches achieve strong performance, they heavily rely on large training, mostly fully sampled, datasets, and may otherwise generalize poorly. In contrast, training-data-free methods such as deep image prior (DIP) adapt directly to individual scans but often fail to fully exploit temporal structure and are prone to overfitting. They are particularly attractive for dynamic MRI due to the limited large, public, high-quality datasets. In this work, we propose a structured DIP framework for dynamic MRI reconstruction that explicitly models spatiotemporal correlations through a low-rank plus sparse (L+S) decomposition. Instead of directly reconstructing the cine image series, we parameterize the low-rank background and sparse dynamic components using two DIP untrained convolutional neural networks, jointly optimized using accelerated extrapolated ADMM (eADMM). This formulation combines the implicit regularization of DIP with the interpretability of classical L+S regularization. We provide a convergence analysis for the proposed eADMM algorithm in the presence of DIP-based nonconvex parameterizations. In particular, we establish a sufficient descent property and show that every cluster point of the generated sequence is a critical point of the associated Lyapunov function. Across various acceleration factors, our numerical results demonstrate that the proposed method consistently outperforms classical reconstruction and existing supervised and unsupervised MRI reconstruction techniques.

Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the stationary covariance assumptions of classical Kalman filters. The Sage-Husa Kalman Filter (SHKF) estimates noise statistics online, but its reliance on a static, scalar forgetting factor forces a strict compromise between steady-state stability and transient responsiveness. We introduce the N-Deep Recurrent Sage-Husa Filter (NDR-SHKF), which replaces this scalar parameter with a vector-valued memory attenuation policy learned by a hierarchical recurrent network operating on whitened innovation sequences. A bifurcated architecture routes shallow recurrent states to capture instantaneous sensor anomalies and deep states to encode sustained dynamic trends, while an auxiliary reconstruction objective prevents feature collapse. The complete filter, including recursive covariance updates, is trained end-to-end via backpropagation through time to directly minimize state estimation error. Evaluations on topologically distinct chaotic attractors demonstrate cross-domain generalization, outperforming purely data-driven baselines that diverge under out-of-distribution dynamics. Furthermore, evaluations on recorded real-world UAV flight datasets validate the framework's practical viability, demonstrating its capacity to bridge transitions into proprioceptive dead reckoning and outperform classical adaptive estimators during sensor outages.

Composed of multiple interconnected pixels controlled by on/off RF switches, the pixel antenna can generate reconfigurable radiation patterns that can be further exploited to construct diverse pilot sequences for effective channel estimation. However, such pilot sequences inherently have rank deficiency, making it difficult to effectively and efficiently acquire the full channel state information (CSI) across all available radiation patterns. To tackle this difficulty, we consider a sparse environment with a limited number of propagation paths for a pixel antenna system, where a user equipped with a pixel antenna transmits only a limited number of pilots to recover the CSI under all radiation patterns. The proposed algorithm exploits the limited number of propagation paths that are invariant with the pixel antenna patterns, and then formulates the full channel estimation as a sparse recovery problem in the angular domain solved by Generalized Approximate Message Passing (GAMP). Moreover, to mitigate the rank deficiency of pilot sequences, we additionally incorporate a Multipath Matching Pursuit (MMP) algorithm for robust initialization. The overall proposed scheme, termed MMP-GAMP, achieves higher estimation accuracy than other algorithm baselines, while requiring lower pilot overhead.

A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented. The approach relies on a one-dimensional convolutional autoencoder trained using a Noise2Noise strategy, requiring neither external spectral libraries nor high signal-to-noise reference spectra for training. From a reduced training subset composed of repeated short-exposure acquisitions, the model learns to reconstruct Raman spectra while efficiently suppressing stochastic noise. The method is evaluated on a heterogeneous mineral sample, using both quantitative spectral fidelity metrics (RMSE, SNR, SSIM) and task-oriented criteria based on unsupervised K-means classification. Results demonstrate that integration times as short as 5 ms per spectrum, which are typically insufficient for reliable interpretation, yield denoised spectra with high fidelity to the reference data while preserving chemically coherent maps. This work provides a practical trade-off between spectral quality and acquisition speed, enabling fast, adaptable Raman workflows compatible with routine laboratory use. It also offers a transferable framework for other one-dimensional spectroscopic modalities.

This paper investigates a multiple unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system equipped with movable antenna (MA) arrays. To align with practical scenarios, we simulate the dynamic roaming of ground users and the three-dimensional deployment of UAVs in the airspace. We aim to maximize the total data rate by jointly optimizing key operational variables, including UAV trajectories, user association, antenna positions, and beamforming. This formulated problem is subject to constraints on transmission power and the sensing signal-to-noise ratio. To address the challenge of dynamically unknown state transitions due to user mobility, the original problem is decomposed into two steps and solved using different algorithms. First, we utilize the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to address the ground-to-air association problem, periodically updating clusters and re-associating during training. The clustering hotspots are used to suggest flight directions for the UAVs. Second, we develop the soft actor-critic algorithm to solve the joint optimization problem of UAV trajectories, antenna positions, and beamforming. Experimental results demonstrate that UAVs equipped with MA arrays outperform those with traditional fixed antenna arrays in ISAC systems, and the proposed optimization strategy effectively enhances communication rates while ensuring sensing performance.

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

The augmented affine projection algorithm (AAPA) has considerably excellent performance for highly colored input signals. However, the direct matrix inversion operation leads to a high computational complexity, especially with high projection order. Inspired by the excellent characteristics of set-membership filtering (SMF), this paper proposes the augmented set-membership affine projection algorithm (ASM-APA), which not only has low computational complexity but also offers improved performance compared with AAPA. Then, the computational complexity and stability of ASM-APA are analyzed, and the condition for maintaining the stability of the algorithm is provided. Finally, in the computer simulation phase, the results of the simulation experiments demonstrated that ASM-APA has superior performance compared to AAPA.

Adaptive filter in complex scenarios demands algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this challenge, we propose a online censoring robust total generalized adaptive filter using improved data-reused method (RTGA-IDROC) algorithm. The proposed RTGA variant possesses the advantages of both the total least squares (TLS) strategy and the robust generalized adaptive (RGA) function. This algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performance across diverse noise environments. Furthermore, to meet the high demand for convergence speed in practical applications, an improved data reuse (IDR) method is introduced, enabling faster convergence in the early stages of iteration without compromising steady-state performance. The increased computational complexity brought by the IDR method is mitigated using the online censoring (OC) strategy. We also modify the OC threshold for real-valued algorithms, as the original threshold was defined for the complex domain. Beyond these algorithmic enhancements, a local stability analysis for the proposed algorithm is provided, and the theoretical steady-state mean-square deviation (MSD) is derived. Finally, simulation experiments in system identification and acoustic echo cancellation (AEC) scenarios validate the superior performance of the proposed algorithm.

Covert quantum communication (CQC) seeks to hide not only message content but also the existence of communication. Existing CQC models usually assume deterministic or worst-case channel conditions, which are difficult to justify in realistic free-space optical and quantum links affected by turbulence, fluctuating background radiance, and stochastic detector noise. We propose a stochastic risk-aware optimization framework for CQC under uncertain physical-layer conditions. By modeling transmissivity and background noise as random variables, we express covertness and reliability guarantees through chance constraints with explicit outage budgets $ε_{\text{cov}}$ and $ε_{\text{rel}}$. This recasts CQC design as a risk-calibrated resource-allocation problem balancing throughput, covertness, reliability, and communication privacy. We derive quantile-based reformulations of the outage constraints, characterize feasible operating regions under stochastic uncertainty, and introduce a complementary risk-adjusted utility formulation to expose throughput-risk trade-offs. The analysis reveals that modest relaxations in acceptable covertness-outage risk can yield large throughput gains, while aggressive optimization may break covertness outside sparse-transmission regimes. Monte Carlo results under log-normal fading and stochastic thermal noise show that the framework expands feasible operating regions, improves covert throughput by more than an order of magnitude, and identifies degradation boundaries beyond which covert operation becomes unreliable. These results move CQC closer to realistic secure quantum networking for free-space, satellite, and low-probability-of-detection applications.

For downlink transmission in massive multi-user multiple-input multiple-output (MU-MIMO) systems, conventional precoding research heavily focuses on reducing the computational complexity of precoding matrix design, while largely overlooking another critical bottleneck: the substantial signal weighting cost incurred by repeatedly applying the precoder to high-speed data streams. To address both challenges simultaneously, this paper proposes a novel sparse precoding framework tailored for fully-digital architectures. Within this framework, from the sum-rate maximization perspective, we design two sparse precoding architectures: a common-support row-sparse architecture and a user-specific row-sparse architecture, so as to reduce the number of multiplication operations required in baseband signal weighting without sacrificing system capacity. For the formulated mixed-integer non-linear programming (MINLP) problem, we rigorously prove, for the first time, that the optimal precoder under both sparse architectures strictly resides in a specific low-dimensional subspace determined by the channel matrices, thereby reducing the dimensionality of the optimization variables. Based on this insight, an alternating optimization algorithm is developed within the weighted minimum mean square error (WMMSE) framework to jointly optimize sparse beam selection and low-dimensional precoding coefficients. The combinatorial beam selection problem is handled using an efficient penalty-based majorize-minimization (MM) method, yielding a low-complexity closed-form solution. Simulation results demonstrate that the proposed scheme achieves near-optimal sum-rate performance while substantially reducing both the precoding computation complexity and the overall signal weighting cost.

Sparse recovery algorithms are of utmost importance for estimation processes in wireless communications. However, communication systems such as massive multiple input multiple output (MIMO) systems are rapidly growing in dimension, which consequently increases the computational complexity of these algorithms. This work proposes a low-complexity strategy for the efficient implementation of the ''atom selection step'' in these greedy sparse recovery algorithms, based on the structural features of these systems. A theoretical justification is presented along with tests using realistic channel data, to demonstrate the computational gain induced by the proposed approach and compare it to the classical sparse recovery approach.

Channel estimation is essential to massive multiple-input multiple-output (MIMO) systems. While recent generative model-based approaches using lightweight diffusion models (DMs) have achieved superior performance, they typically rely on a single data-driven prior, which limits their adaptability to varying channel distributions in real-world scenarios. To address this deficiency, we propose a mixture-of-experts (MoE) diffusion model (DM) framework combined with variational Bayesian inference. Specifically, our approach employs multiple pre-trained DMs, with each trained on a specific type of propagation channels. We then propose a probabilistic graphical model in which the channel is modeled as a latent variable drawn from one of these candidate generative priors with a certain probability. By integrating variational Bayesian inference with DM-based data priors, the underlying channel along with the expert indicator variable are jointly inferred, thus enabling automatic model adaptation for channel estimation. The effectiveness of our approach is evaluated on 3GPP CDL channels. Simulation results demonstrate that our proposed approach achieves a clear performance improvement over the standard DM-based method that employs a single prior trained on aggregated data from all channel types, particularly when the channel samples from different propagation environments are imbalanced.

Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (EEG) features contribute to their characterization within an interpretable computational framework. In this study, we build on an experimental paradigm developed in our previous work, which enables controlled comparison between task-constrained self-initiated shifts and externally instructed shifts under identical visual stimulation. Within this setting, we investigate whether preparatory EEG activity can distinguish these two types of attention shifts. We adopt a machine learning-based approach and conduct two complementary analyses: (1) a performance-oriented assessment of frequency-specific topographic patterns, and (2) a model-based feature attribution analysis using SHapley Additive exPlanations (SHAP). These analyses provide a structured view of how spectral features across regions of interest contribute to model behavior. Our results demonstrate reliable within-subject classification performance, indicating that preparatory EEG activity contains subject-specific discriminative information within this paradigm. The analysis shows that higher-frequency bands and frontal regions contribute strongly to model decisions, although such contributions should be interpreted cautiously due to the potential influence of non-neural artifacts in high-frequency EEG signals. Overall, this work highlights the value of interpretable machine learning for analyzing subject-specific EEG signal patterns in a controlled experimental setting, with potential applications in personalized and asynchronous brain-machine interface systems.

This paper proposes a framework for fast signal acquisition based on deterministic non-uniform sampling, with emphasis on multi-coset architectures and receivers driven by known synchronization sequences, pilots, or preambles. Unlike conventional sampling theory, which is formulated from a waveform-reconstruction perspective, the proposed approach is derived from the observation that acquisition is fundamentally a parametric inference problem in delay-Doppler space. Accordingly, the objective is not to reconstruct the full Nyquist-rate signal, but to preserve the statistics required for detection and estimation. The paper formulates compressed-domain acquisition through a generalized likelihood ratio test and shows how multi-coset sampling leads to reduced correlator structures operating directly on the retained samples. An offline exhaustive design procedure is introduced to select the coset pattern for a given sampling ratio by minimizing a cost that jointly enforces peak isolation in the acquisition surface and uniform retained-energy coverage over the delay search interval. The framework is evaluated on 5G NR synchronization using the PSS/SSS signals under a worst-case Doppler scenario. Results show that substantial reductions in mean acquisition time can be achieved relative to uniform sampling, with measured gains ranging from 2.8x to 34.2x, depending on the selected compression ratio. The corresponding delay and Doppler root-mean-square errors quantify the estimation penalty introduced by aggressive sample reduction. These results demonstrate a clear complexity-performance trade-off and confirm the potential of multi-coset sampling for fast synchronization-oriented receivers.

Signal integrity (SI) analysis in printed circuit board (PCB) interconnects faces increasing complexity due to diverse integrated circuit (IC) buffer technologies, varying operating conditions, and manufacturing tolerances. Existing machine learning (ML) surrogate models for predicting SI metrics such as the inner eye contour, eye-height (EH), eye-width (EW), and transient waveform features typically rely on fixed buffer parameters, requiring costly new data generation and retraining cycles for every technology shift. This paper introduces a buffer-parameterized ML surrogate modeling methodology capable of handling cross-technology variations without retraining by treating IC buffer characteristics, e.g., clock frequency, supply voltage, rise/fall times, jitter, and internal resistors and capacitors, as dynamic model inputs alongside PCB parameters. To identify the optimal surrogate architecture for this high-dimensional space, a comprehensive benchmarking study compares tree-based methods (RFR/GBM), kernel methods (SVR/KRR), Gaussian process regression (GPR), and neural networks. The framework is subsequently validated on a complex interconnect with 44 design parameters. Results show that while anisotropic GPR excels in low-data regimes, neural networks heavily outperform other models on large datasets. Finally, the practical value of the ML surrogate models is demonstrated through a cross-technology design space exploration and optimization scenario, showcasing massive computational speedups for eye mask compliance checking compared to simulation.

Acquiring channel knowledge is required by many applications. For instance, handover in cellular networks is mainly decided based on the knowledge of pathloss. In contrast to traditional statistical distance-determined models that might provide misleading pathloss estimates, researchers started to explore deep learning methods recently to accurately estimate the radio map that characterizes the spatial distribution of pathloss according to the specific physical wireless propagation environment. However, existing works mainly focused on 2D radio map estimation by assuming that all receivers are at the same height. In fact, radio maps could be significantly different at different receiver heights, highlighting the importance of 3D radio map estimation. In this paper, we first propose a method to embed height information into 2D images, and then propose a general 2D radio residual network (R$^{2}$Net) for 3D radio map estimation. Since pathloss exhibits different characteristics in indoor and outdoor scenarios, we specifically propose R$^{2}$Net-In for indoor scenarios and R$^{2}$Net-Out for outdoor scenarios to better capture penetration loss and diffraction loss, respectively. Extensive experimental results show that our R$^{2}$Net significantly outperforms the state-of-the-art benchmarks in terms of estimation accuracy, computational and storage costs, and inference speed. In addition, due to the lack of publicly available 3D radio map datasets, a 3D indoor radio map dataset (3DiRM3200) is created, which took more than $1,000$ labour hours. The dataset and codes will be available at https://github.com/lighttime2023/3DiRM3200.git.

Future 6G systems are expected to exploit upper midband spectrum in frequency range 3 (FR3) not only for high throughput communications, but also for sensing services such as localization, detection, and situational awareness. The following paper develops a concrete path from today's coverage-oriented deployments to FR3 networks that treat sensing as a native function. We first show how existing FR2 radars can be time-multiplexed and coordinated under a $6$G medium access control as radar-as-a-service, forming a bridge between legacy sensing and network-managed integrated sensing and communications (ISAC). We then propose a hierarchical FR3 beam-alignment strategy in which coarse access occurs at lower frequencies and refinement occurs at upper FR3, and quantify the resulting sensing and communication capabilities via range-angle Cram{é}r-Rao bounds in the near field. We identify intra- and inter-beam squint phenomena specific to wideband FR3 arrays, and discuss design approaches to mitigate them. On the signal-processing side, we argue that FR3 sensing cannot rely solely on pilot resources and discuss how much sensing information can be extracted from payload resource elements. We further highlight the role of calibrated FR3 channel simulators and real-time models as the core of wireless digital twins for training and evaluating ISAC algorithms, and discuss how massive MIMO and dense or distributed deployments at FR3 naturally act as large reconfigurable sensor arrays.

EEG foundation models aim to learn reusable representations across heterogeneous paradigms, yet existing approaches often use uniform adaptation mechanisms and are typically reported under separate downstream fine-tuning protocols. In this work, we first analyze dense EEG Transformers from two complementary perspectives. Gradient similarity across six downstream datasets reveals substantial optimization conflicts among EEG paradigms, while CKA analysis on mixed-paradigm batches shows a consistent depth-wise transition: shallow layers preserve stronger cross-paradigm similarity, whereas deeper layers become increasingly specialized. Motivated by these findings, we propose \textbf{PRiSE-EEG}, a prior-guided EEG foundation model with CKA-calibrated Depth-Stratified Experts. PRiSE-EEG forms continuous multi-channel EEG patches using weak static cortical and network priors and dynamic short-time channel interactions, then allocates shared and specialized experts across MoE Transformer blocks according to a sigmoid mapping from layer-wise CKA sharedness. This design preserves common EEG regularities in early blocks while assigning more specialized capacity to later task-specific transformations. Experiments on 12 public EEG benchmarks show strong cross-paradigm performance under matched protocols. Compact ablations further show that CKA-derived expert allocation improves over dense Transformers, uniform MoE, and manually fixed shared-specific expert ratios.

Seamlessly unifying communication and sensing, sixth-generation (6G) networks are poised to transform into intelligent platforms with high spectral-energy efficiency and real-time environmental awareness. In the low-altitude economy, unmanned aerial vehicles (UAVs) enable air-ground integrated sensing and communication (ISAC) for applications such as logistics and inspection, yet most studies focus on single-UAV or homogeneous-agent designs. In contrast, this paper proposes a multi-UAV cooperative ISAC system that enables heterogeneous-agent collaboration between multiple UAVs and a ground base station (BS) for joint target sensing, tracking, and communication. The system is formulated as a posterior Cramer-Rao bound (PCRB) minimization problem under communication performance constraints, utilizing joint trajectory-beamforming optimization. To tackle the NP-hard nature of this problem, we design a curriculum-based heterogeneous-agent proximal policy optimization (C-HAPPO) algorithm, where curriculum learning guides progressive policy refinement and Kronecker/QR decomposition mitigates action dimensionality. Simulation results show that the proposed approach achieves more than a 30% improvement in sensing performance, faster convergence, and higher tracking accuracy than existing baselines, demonstrating its scalability and effectiveness for complex multi-UAV ISAC scenarios.

We demonstrate a data-driven framework for emulating high-speed VCSEL-based 4-level Pulse Amplitude Modulation (PAM-4) optical interconnects using bidirectional Long Short-Term Memory (Bi-LSTM) networks. Unlike conventional rate-equation models, which are computationally intensive and often require difficult parameter tuning, our approach utilizes experimental waveforms to learn the end-to-end system dynamics. By employing transfer learning and weight interpolation, we extend the model to new operating regimes with a 20-fold reduction in computation time compared to independent training, while maintaining normalized mean squared error below 0.04. This emulator provides a rapid, accurate tool for the design and optimization of short-reach optical links.

Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generative models, existing time series libraries are mainly engineered for discriminative models, with standardized workflows for specific tasks, such as optimizing Mean Squared Errors for time series forecasting. This rigid structure is fundamentally incompatible with the distinct and often complex paradigms of generative models (e.g., adversarial training, diffusion processes), which learn the underlying data distribution rather than a direct input-output mapping. To this end, we proposed GenTS, a comprehensive and extensible benchmark library designed for systematic assessment on generative time series models. GenTS features a unified data preprocessing pipeline, a collection of versatile models, and panoramic evaluation metrics. Its modular design also enables the researchers to flexibly customize beyond our built-in datasets and models. Based on GenTS, we conducted benchmarking experiments under diverse tasks, accordingly offering suggestions for model selection and identifying potential directions for future research. Our codes are open-source at https://github.com/WillWang1113/GenTS. The official tutorials and document are available at https://willwang1113.github.io/GenTS/.

Reference media are widely used in distorted-Born-approximation-based GPR imaging to represent partially known propagation effects. When the true host background differs from the chosen reference medium, the difference enters the observations and propagates into anomaly estimates. For single-snapshot FDA-MIMO-GPR, this paper establishes a reference-state observation model under the distorted Born approximation and defines that difference as the reference--background medium residual, namely, the effective residual between the reference medium and the physical background medium. Hereafter, this quantity is abbreviated as the reference--background residual. Its response is derived from the Cole--Cole dispersive mapping, the reference propagation kernels, and the FDA frequency--transmit organization. The paper then constructs its observation-domain covariance, analyzes the off-diagonal channel-block structure, and uses a standard Tikhonov estimator to show how the response transfers to reconstruction error and covariance over an anomaly candidate region. Numerical results show pronounced cross-frequency and cross-channel covariance under mismatched reference states. After Tikhonov reconstruction, these structures appear as low-dimensional, concentrated pseudo-anomaly errors. Right-hand-side coherence and inter-channel correlation arise mainly because multiple transmit--receive channels jointly observe the same residual field, while FDA space-frequency coding determines their organization in the observation and reconstruction domains. The reference--background residual should therefore be modeled explicitly in reference-state selection, background suppression, and channel-covariance analysis for single-snapshot FDA-MIMO-GPR.

This work establishes a framework of near-field communication under different array geometries of extremely large-scale multiple-input multiple-output (XL-MIMO). We first formulate the near-field spatial non-stationary channel model which is characterized by the distance between the user and each antenna on uniform and modular curved arrays. By fixing the total number of antennas while varying the degree of curvature, we investigate a fair case where the horizontal arc length of the curved array is the same as the planar array. We explicitly unveil the non-trivial impact of array curvature on extending the near-field region for cell edges. Then, for arbitrary array geometries and arbitrary-field channels, we estimate the spatial-domain channel by tackling a compressed sensing problem with a learned regularizer. Without relying on specific codebooks, we propose a denoising autoencoder (AE)-aided approximated message passing (AMP) algorithm and provide the corresponding theoretical replica bound. Finally, based on the estimated channel, we propose an optimization algorithm to maximize the sum user rate for sub-connected XL-MIMO systems by jointly designing the array geometry and hybrid precoding in the downlink. Numerical results demonstrate that the proposed AE-AMP algorithm can effectively estimate the spatial non-stationary near-field channels with robustness and generalities compared to several conventional and deep-learning-based benchmarks. The improvement of data rate by using modular curved arrays with the estimated channel is also validated.

Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, most existing methods rely on unimodal deep learning architectures that independently process either one-dimensional (1D) temporal signals or two-dimensional (2D) time-frequency representations, limiting robustness and generalization. To address this issue, this paper proposes a hybrid framework integrating 1D and 2D convolutional neural networks (CNNs) within a unified end-to-end architecture. The 1D branch extracts temporal and morphological features from raw ECG signals, while the 2D branch captures discriminative spectral information from time-frequency representations. An attention-guided fusion mechanism dynamically weights both modalities according to input characteristics, overcoming the limitations of conventional static fusion strategies. The framework was evaluated on three benchmark datasets (ECG-ID, MIT-BIH, and PTB), including healthy subjects and patients with cardiac pathologies, achieving identification accuracies of 99.56%, 100.00%, and 99.89%, respectively. To assess long-term biometric permanence, experiments were also conducted on the multi-session Heartprint dataset spanning ten years. The proposed approach achieved same-session accuracies of 98.54% (S1), 99.09% (S2), 94.93% (S3R), and 96.08% (S3L), while cross-session evaluations reached 56.33% (S1-S2) and 53.27% (S2-S3R), demonstrating the ability to capture stable biometric signatures over time. The optimal configuration combines InceptionTime for 1D processing, ResNet-34 for 2D analysis, and attention-based fusion. Ablation studies confirm that the proposed attention mechanism consistently outperforms conventional fusion approaches. Overall, the proposed framework provides a robust, scalable, and high-performance solution for ECG biometric recognition.

We experimentally evaluated the sensing-communication trade-off from the fixed-point precision MIMO equalizer using FPGA. At 7-bit, noise floor drops 100x and angular error 63%, but the communication performance saturates while the hardware complexity rises.

Spatially selective active noise control (SSANC) hearables aim to attenuate noise from certain directions at the eardrum while preserving desired speech arriving from selected directions. Existing SSANC systems typically assume an accurate estimate of the secondary path from the loudspeaker to the inner error microphone. In practice, however, this path varies across users and device fits, which can degrade performance and compromise system stability. This paper proposes a robust soft-constrained optimization framework that computes a single control filter by minimizing the average cost over a set of secondary path estimates derived from human measurements. Simulations and experiments on a real-time control platform show that the proposed approach slightly reduces mean performance relative to the matched case but substantially narrows the performance spread under secondary path mismatch. The proposed framework therefore provides a practical design strategy when accurate secondary path estimates are unavailable.

Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these signals requires invasive surgery. While recent studies have attempted to estimate iEEG from non-invasive scalp EEG, most rely on patient-specific models, creating a circular dependency: if surgery is required to collect training data, the non-invasive model offers limited practical benefit. In this study, we address the challenge of cross-subject iEEG reconstruction by predicting intracranial signals for unseen patients using models trained on other individuals. We propose CAST (Cross-Attention Spatial-Temporal Transformer), a machine learning framework that translates scalp EEG into multi-channel iEEG waveforms through a two-stage transfer learning strategy. First, a temporal encoder extracts multi-scale neural representations at three different resolutions. Then, because electrode placements vary substantially across patients, a channel-aware decoder is calibrated using only a few minutes of data from the target subject. We evaluated the proposed method using leave-one-subject-out cross-validation on two public datasets comprising 1,282 iEEG channels. Experimental results demonstrate that CAST reconstructs cortical signals located near the scalp surface substantially better than deep subcortical activity. In highly observable sensorimotor regions, the model achieved peak correlations of up to r=0.864 in the precentral gyrus. Furthermore, with a channel selection strategy, CAST obtained a mean correlation of r=0.545 on viable subjects, outperforming previous within-subject baselines. These findings indicate that cortical iEEG signals can be reconstructed for unseen subjects from scalp EEG without extensive patient-specific training, and that only a brief calibration phase is sufficient to adapt the model to new hardware configurations.

We introduce UPSim (UxNB Propagation Simulator), a ray tracing-calibrated, semi-deterministic solution for spatially consistent FR3 air-to-ground propagation modeling in uncrewed aerial vehicle (UAV) networks. Instead of launching rays for every receiver position, UPSim derives deterministic visibility regions from 3D building geometry via shadow projection. It then augments these regions with line-of-sight (LOS) state-specific and altitude-aware path loss, correlated large-scale fading, and small-scale fading. Calibration and validation against FR3 ray tracing data using the global 3D-GloBFP building dataset demonstrate that UPSim accurately reproduces empirical channel distributions. Furthermore, the resulting maps support route-based analysis of channel evolution over complex urban layouts, exposing critical trajectory-level statistics such as outage distances. Consequently, UPSim offers a highly scalable, practical middle ground between computationally expensive full ray tracing and purely stochastic channel generation for mobility-aware planning and radio-map construction in aerial access scenarios.

High-density LED arrays enable high-speed transmission in image-sensor-based visible-light communication (VLC) systems. However, when optical spots become blurred and spatially overlapped due to focal shift, resolution limitations, or interference, severe inter-symbol interference (ISI) occurs, significantly degrading decoding performance. Furthermore, radial distortion introduces geometric deformation of the LED grid, while vignetting leads to incomplete and asymmetric spot shapes at the periphery, both of which further hinder reliable signal detection. Existing methods mitigate ISI by reducing LED transmission signaling density. This paper proposes a robust decoding framework that maintains full LED signaling density. We introduce a pilot-aided geometric recognition method that uses a PSF-constrained Hough transform and circle-center alignment refinement. \textbf{In addition, radial distortion correction and vignetting-aware compensation are incorporated to restore geometric consistency and suppress edge-related detection errors.} By leveraging prior structural knowledge from pilot frames, the system effectively separates overlapping LED signals under severe optical distortion. Experimental results on a real-world VLC testbed confirm that the proposed method achieves superior decoding accuracy and throughput compared to conventional Hough-based and low-density baseline methods. The results highlight its potential for high-efficiency VLC applications in interference-prone environments.

Monitoring deployments often require reliable long-range wireless links to intermittently upload sensor logs and short video snapshots. Wi-Fi HaLow (IEEE~802.11ah) is a promising candidate due to sub-1 GHz propagation and bandwidth-flexible PHY modes. This summary paper reports a field characterization organized around three deployment-driven regimes: (i) point-to-point Non-Line-of-Sight (NLoS) links; (ii) point-to-point Line-of-Sight (LoS) links over several-hundred-meter distances; and (iii) LoS mesh networking with fixed relay nodes for range extension. Using commodity HaLow dongle-class nodes in all regimes, we report application-layer goodput and monitoring-centric update latency based on transferring a representative ``heavy'' object (a $\sim$30 s video file). The measurements reveal (a) a clear bandwidth--range tradeoff and an NLoS coverage boundary around $\sim$120 m, (b) gradual throughput decay under LoS up to 814 m in single-hop with 0.15 Mbps at the farthest point, and (c) kilometer-class extension under LoS when fixed relays are introduced, reaching 901 m (two fixed relays) and 1110 m (three fixed relays

Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and interaction dynamics that remote sensors cannot replace. However, unimodal tactile perception is inherently limited by its sparse spatial coverage and lack of global semantic context. With the recent explosion in deep learning and large language models, integrating tactile with vision and language has become essential to bridge physical interaction with semantic reasoning, leading to the emergence of Multimodal Tactile Fusion. Despite rapid progress, the existing researches remain fragmented across disparate datasets, sensing modalities, and tasks, lacking a unified theoretical framework. To address this gap, this paper provides a comprehensive survey of multimodal tactile fusion research up to the first quarter of 2026. We propose a hierarchical taxonomy that organizes the field into two primary dimensions: multimodal datasets and multimodal methods. On the data side, we categorize resources ranging from Tactile-Vision datasets, Tactile-Language datasets, Tactile-Vision-Language datasets, and Tactile-Vision-Other datasets. On the method side, we structure prior work into three core pillars: (1) Multimodal Perception and Recognition, which focuses on object understanding and grasp prediction; (2) Cross-Modal Generation, focusing on bidirectional translation between tactile, vision, and text; and (3) Multimodal Interaction, emphasizing feedback control and language-guided manipulation. Furthermore, we summarize representative tactile sensing hardware, review commonly used evaluation metrics and benchmark settings, and discuss current challenges and promising future directions.

Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C$^{2}$F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C$^{2}$F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training, and eXplainable AI to ensure interpretability and trust, which are vital in cybersecurity. This paper presents an interesting review of current AI-based cybersecurity trends, focusing on intrusion detection approaches and aiming to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.

Second-generation Starlink Direct-to-Cell (DTC) satellites carry an additional payload for direct cellular phone connectivity whose unintended electromagnetic radiation (UEMR) at sub-300 MHz frequencies has not been individually characterised. We reanalyse 112,534 detections from 1,806 Starlink satellites observed with the Engineering Development Array version 2 (EDA2) at 21 frequencies between 72.685 and 234.375 MHz (Grigg et al. 2025), separating 175 DTC and 1,623 Ku-only v2-Mini comparison satellites via the McDowell General Catalogue (McDowell 2020). DTC satellites emit a range-corrected flux density 1.45x that of the Ku-only comparison (Cliff's delta = +0.30, p = 2.6e-11). At 230.469 MHz the XX detection fraction reaches 0.811 against a 0.481 baseline (p ~ 1e-274), and 11 of 21 frequency channels show Benjamini-Hochberg-significant polarisation anomalies. The DTC population is brighter in eclipse than in sunlight (illuminated/eclipsed flux density ratio 0.47) while the Ku-only comparison shows the opposite sense (1.18); the reversal persists across altitude, sub-satellite latitude, frequency, and launch-epoch matching. The reversal strongly disfavours UEMR mechanisms that scale monotonically with instantaneous solar photocurrent and favours an active on-board source whose effective duty cycle is larger at lower equilibrium temperature. Within the 230.469 MHz coarse channel, fine-channel inspection isolates the excess to a single ~24 kHz bin near 230.627 MHz, tail-driven and absent at five control channels. Three falsifiable mechanism-discrimination tests show this feature is not coincident with the LOFAR-resolved Bassa et al. (2024) clock fundamentals, is unresolved at the EDA2 24 kHz resolution, and is heterogeneously expressed across the v2-Mini fleet rather than driven by a few permanently bright units or by uniform thermal scaling.

We develop a class of data-adaptive shrinkage estimators for high-dimensional covariance estimation in which the shrinkage target is a Reynolds projection of the sample covariance under a finite symmetry group selected from a candidate library by held-out predictive performance. The class generalizes the convex shrinkage estimator of Ledoit and Wolf by replacing the scalar-identity target with a structured target derived from a symmetry group when one is available, and generalizes the group-symmetric maximum-likelihood estimator of Shah and Chandrasekaran by combining structural targeting with adaptive convex shrinkage and by selecting the group from data rather than treating it as prespecified. A two-tier procedure performs the group selection: a universal per-candidate evaluation based on held-out negative log-likelihood, optionally preceded by a domain-specific step that constructs the candidate library from structural priors. We establish a finite-sample regret bound for the held-out calibration of the convex combination weight, an oracle inequality for the data-driven group selection, and a quantitative sufficient-match condition under which the proposed estimator dominates Ledoit-Wolf shrinkage in Frobenius mean-squared error. The procedure is illustrated on six real-data problems spanning finance (S&P~500 daily returns), climate (NOAA OISST sea-surface temperature anomalies), genomics (TCGA-BRCA gene expression), radio signal processing (RadioML 2018.A), astronomical imaging (Galaxy10 DECaLS), and natural image patches (CIFAR-10 with a CIFAR-10.1 distribution-shift companion). An empirical comparison is also made against the Bayesian permutation-symmetry estimator of Chojecki and colleagues. Outside the few-shot regime, where structural priors carry the most information per observation, Ledoit-Wolf shrinkage remains the appropriate baseline.

This paper develops a fault detection and identification (FDI) method for nonlinear control-affine systems under simultaneous actuator and sensor faults. We adopt a geometric approach to study the isolability of faults in the sense of the principal angles between subspaces corresponding to each actuator and sensor fault. As for the fault identification, a hybrid estimator that consists of a Luenberger-like observer with contraction guarantees is developed. Moreover, neural networks are embedded in the mentioned observer to estimate actuator and sensor faults. Considering that the training dataset for neural networks cannot be representative of every fault scenario, the last layer of each network is adapted using mirror descent-based laws. The mirror descent-based adaptive laws impose isolability conditions for fault channels and do not assume a quadratic parameter estimation space to consider the geometry of the fault subspaces. A Lyapunov-based analysis establishes that the state and parameter estimation errors are uniformly ultimately bounded. The effectiveness of our proposed FDI method is illustrated on the 3-axis attitude control system of a spacecraft.

Bluetooth Core Specification v6.0 introduces Channel Sounding (CS) as a standardized high-accuracy ranging primitive for Bluetooth Low Energy (BLE). However, standard CS usage remains tied to per-pair LE asynchronous connection logical transport (LE ACL) connections, which adds initiation overhead, limits concurrent partners, and transfers results over the connection itself. We present a connectionless CS architecture that combines the LE CS Test command with Periodic Advertising with Responses (PAwR). A Central Orchestrator, a Gateway, and synchronized Tag/Anchor devices coordinate measurement configurations and aggregate results at the application layer. Each device derives its role, channel sequence, and response slot assignment from its device index and a Peer-to-Peer Assignment Matrix distributed via PAwR. The deterministic channel sequence prevents same-step collisions across parallel CS procedures, while matrix updates reconfigure arbitrary device-to-device pairings within a PAwR subevent group. A compact data plane omits fields recoverable from the shared measurement configuration and reduces the serialized ranging-data payload by approximately 69%, enabling result reporting through PAwR response slots. A proof-of-concept evaluation on the Nordic nRF54L15 platform shows that deterministic channel management eliminates the collision-induced outliers observed under simulated dense-deployment channel overlaps. At a 1 s update cycle, the architecture reduces steady-state active charge by 40-48% relative to a fair connected baseline and cuts per-switch initiation overhead by approximately 98%. Under per-cycle partner switching, these effects combine to up to 88% lower total charge over a 24 h horizon. An empirical timing model projects a capacity upper bound of 16,384 active devices per PAwR train at four CS procedures per device per cycle, 37 channels, and a single antenna path.

The reconfigurable intelligent surfaces detection and identification (RISs-ID) is a critical process that enables a base station (BS) to adaptively assign the appropriate RIS to a given user equipment (UE). This work proposes a novel modulation scheme to enhance the reliability of RIS-ID by reducing the miss detection and false-alarm probabilities. Specifically, we leverage the RIS's passive beamforming gain to enable over-the-air modulation of the RIS ID, combined with passive beam sweeping to extend detection coverage in angular space. The proposed modulation scheme is validated through computer simulations and prototype experiments, demonstrating its effectiveness in reducing miss-detection and false-alarm probabilities.

Integrated Sensing And Communication (ISAC) is recognized as a key enabler for future 6th Generation (6G) networks, combining communication capabilities with pervasive sensing. In such systems, the estimation of the Doppler shift plays a crucial role for target characterization. However, typical real-world ISAC scenarios largely involve bistatic or multistatic configurations and mobile ISAC nodes. Under these conditions, Doppler estimation becomes particularly challenging, as clock asynchrony between the Transmitter (TX) and the Receivers (RXs), combined with their mobility, introduces additional Doppler components and phase offsets that distort or disrupt the target-induced frequency shift. Existing works have considered these challenges separately or relied on external reference reflectors. In this paper, we present the first method to estimate the Doppler frequency of a target with mobile and asynchronous ISAC nodes in a multistatic configuration, considering the case of a mobile TX and multiple static RXs, and without leveraging any external reflector. By leveraging the invariance of the phase offsets across multipath components and exploiting geometrical relationships, we show that the problem is solvable if at least 4 RXs are present. We evaluate the proposed solution through numerical simulations in various scenarios, showing that it is a valid approach for estimating target Doppler shifts in unsynchronized multistatic ISAC deployments with mobile nodes.

We analyze multi-user fluid antenna systems with continuous positioning over a track of length L under a spatial correlation model, where exact performance distributions become analytically intractable. We develop a level-crossing-rate (LCR) framework that yields asymptotically exact approximations and tight bounds for the cumulative distribution function (cdf) of the optimized metric S* = sup_{0 <= l <= L}, where S(l) denotes the performance metric at antenna position l. For a single fluid antenna, we characterize the cdfs of signal-to-noise ratio (SNR), signal-to interference ratio (SIR) and signal-to-interference-plus-noise ratio (SINR) under Rayleigh fading and extend the approach to Ricean desired channels. We further treat two multi-antenna receiver layouts with maximum-ratio combining: (i) a fluid antenna with a fixed antenna and (ii) a two-element moving array, deriving new LCR results for the practically important case where array-element correlation and positional correlation are inherently coupled. The analysis provides actionable insights: high-threshold tail probabilities scale linearly with L, we derive the required L to neutralize a co-channel interferer, and we show that about one wavelength of movement can reduce outage by three orders of magnitude. Monte Carlo results validate the accuracy across the considered scenarios and regimes.

This paper provides a thorough mathematical analysis of continuous movable antenna (MA) arrays. Focusing on the multiple antenna case, we consider a linear antenna array with multiple fixed antenna elements that moves along a line. We assume a full, spatially coherent correlation model and continuous positioning of the array. We provide asymptotically exact approximations to the upper tail of the cumulative distribution function (cdf) of the signal-to-noise ratio (SNR), considering both correlated and uncorrelated antenna elements in the array. We also obtain a novel closed-form expression for the level crossing rate (LCR) of the SNR under correlated array elements, where a non-separable two-dimensional correlation is present. The analysis is validated through simulations, confirming both the accuracy of the LCR expressions and the tightness of the cdf bounds in the upper tail. Numerical results show that the proposed MA array outperforms single fluid antenna and fixed array systems, with reduced inter-element spacing providing further performance gains.

Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) due to its spectral efficiency and compatibility with modern wireless standards. In multi-target and clutter-rich environments, however, payload-based OFDM-ISAC can suffer from data-dependent sidelobes induced by non-constant-modulus modulation symbols. To overcome these limitations, this paper proposes a region-of-interest mismatched filter (ROI-MMF) that suppresses sidelobes within a prescribed delay region while preserving the mainlobe response. By leveraging the Woodbury identity, the proposed design admits an efficient closed-form implementation whose complexity scales with the ROI size rather than the number of subcarriers. We theoretically provide the ranging mean-square error (MSE) of the designed ROI-MMF, which shows the superior performance compared to conventional matched filtering (MF) and reciprocal filtering (RF) sensing receivers. Simulations across various constellations show that the proposed sensing receiver achieves a ranging MSE approaching the Cramér-Rao bound (CRB), which notably confirms that our design preserves the target ranging performance even under the non-constant-modulus constellation. Finally, the framework is experimentally validated with our over-the-air OFDM-ISAC testbed.

Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinical risk stratification tools rely primarily on non-imaging data and exhibit limited predictive performance. Point-of-care lung ultrasound (LUS) offers a sensitive, noninvasive window into the pulmonary congestion that characterizes CHF decompensation, yet its prognostic utility for readmission prediction remains largely unexplored. We present a pilot feasibility study, the first systematic machine learning study using B-mode LUS acquired during hospitalization to predict 30-day CHF readmission. Quantitative spatiotemporal embeddings are extracted from a pretrained Temporal Shift Module (TSM) ResNet-18 encoder, and interpretable biomarker features are separately evaluated. Through structured ablations over lung view, temporal representation, multi-view fusion, and cross-lung augmentation, we identify the key imaging factors driving readmission risk. Our findings reveal that (1) dependent lower-lung regions (Left-3, Right-3) carry the strongest prognostic signal, consistent with their greater susceptibility to hydrostatic congestion; (2) temporal difference features between sequential examinations substantially outperform single-timepoint representations, highlighting the importance of capturing disease trajectory; and (3) multi-view feature concatenation yields the best overall performance, with our top MLP model achieving an F1 score of 0.80 (95% CI: 0.62-0.96). Biomarker analysis further reveals that pleural-line abnormalities, including breaks and indentations, are as informative as the canonical A-line and B-line markers. These results support POCUS-derived biomarkers as practical, interpretable tools for noninvasive CHF risk stratification.

Coded computing is a distributed paradigm that uses coding theory to introduce \textit{redundancy} and overcome bottlenecks in large-scale systems. In the same vein, randomized numerical linear algebra employs probabilistic methods to \textit{compress} and accelerate linear algebraic operations, addressing challenges in high-dimensional data analysis. This article reviews the foundations of both fields and presents distributed schemes that combine techniques from both to speed up optimization and machine learning algorithms, in the presence of slow or non-responsive servers. Along the way, we touch on various related topics and mathematical concepts.

AI-native 6G visions increasingly invoke wireless foundation models, large multimodal models, and wireless world models as the natural endpoint of AI-native networking, drawing an analogy to recent developments in large language models (LLMs). We argue that this analogy is structurally incomplete. The success of LLMs is based on a broad, reusable, and largely self-contained tokenized data substrate, whereas the wireless domain lacks an equivalent data foundation. Unlike text, code, or images, wireless data such as CSI tensors, IQ samples, or scheduler logs are not self-contained: their meaning is configuration-dependent, simulator-conditioned, task-disaggregated, and weakly grounded in operational feedback, all structural bottlenecks that undermine current pre- and post-training recipes. We therefore argue that monolithic models, including mixture-of-experts (MoE) and wireless world models, are not the most realistic near-term path toward deployable AI-native networks. Instead, emerging evidence points toward composable and agentic network architectures, where general reasoning models orchestrate specialized signal processing models, classical algorithms, digital twins, standards-aware retrieval, and safety checks through explicit programmable interfaces.

Audio super-resolution (SR), also referred to as bandwidth extension (BWE), aims to reconstruct high-fidelity signals from low-resolution (LR) or band-limited (BL) observations, an inherently ill-posed task due to the ambiguity of missing high-frequency (HF) content. This survey provides a comprehensive overview of the field, with a particular focus on the paradigm shift from discriminative mapping to modern generative modeling. We first review early discriminative deep neural network (DNN) models, which formulate BWE/SR as a deterministic mapping problem and are prone to regression-to-the-mean effects and spectral over-smoothing. We then systematically review generative approaches, including autoregressive (AR) models, variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion and score-based models, flow-based methods, and Schrödinger bridges. Across these approaches, we examine key design aspects, including representation domain, architecture, conditioning mechanisms, and trade-offs among reconstruction fidelity, perceptual quality, robustness, and computational efficiency. Furthermore, we discuss emerging directions involving large language models (LLMs) and multimodal foundation models, and highlight open challenges in perceptual evaluation, phase modeling, and real-world generalization. By providing a structured taxonomy and unified perspective, this survey establishes a comprehensive foundation and offers a practical roadmap for advancing BWE/SR from deterministic point estimation toward distribution-aware generative modeling.

Quantum key distribution (QKD) provides information-theoretic security and satellite-based quantum key distribution (SatQKD) has demonstrated the potential to extend this communication security to intercontinental scales. However, atmospheric turbulence induces significant distortion in the spatial distribution of received optical beams, while background noise remains approximately uniform across the detector plane. As a result, single-element qubit (quantum bit) detection can be frequently dominated by noise due to the random spatial pattern of the imaged wavefront, thereby degrading the system performance. To address this limitation, we propose to exploit the spatial degrees of freedom of single-photon detector arrays to reject the excessive noise while adapting to channel variations induced by turbulence. We develop a threshold-based selection method that only activates detector elements that have higher probability of registering qubits. We evaluate the performance of the proposed noise-rejection QKD schemes using Monte Carlo simulations considering the impact of diffraction and atmospheric turbulence on the transmitted optical beam in the presence of background and dark noise. The results show that, compared to conventional schemes, the proposed noise-rejection strategy effectively reduces the quantum bit error rate (QBER) and improves the secret key rate (SKR) performance, while the performance gains depend on the turbulence condition. These findings demonstrate the potential of adaptive array receiver design to enhance the robustness of the SatQKD system under realistic atmospheric conditions.

Fluid reconfigurable intelligent surfaces (FRISs) have recently emerged as a promising paradigm for wireless communications, wherein the reflecting elements can dynamically select their effective radiating positions from a dense preset grid, thereby introducing an additional degree of freedom. In contrast to conventional RIS architectures, FRISs can achieve spatial diversity with fewer physical elements. However, beyond the cascaded channel structure, FRIS-assisted systems are also affected by uncertainties arising from element-position mismatches caused by calibration inaccuracies or motion errors, which may degrade channel state information. To the best of our knowledge, channel estimation (CE) for FRIS-assisted systems under position uncertainty remains unexplored. To fill this gap, we propose a CE framework for a multi-user FRIS-assisted uplink system based on a two-time-scale FRIS configuration protocol that captures both reflection phase-shift and element-motion dynamics. By capitalizing on orthogonal pilot sequences and tensor modeling, we derive a closed-form solution that jointly estimates the individual channels and the motion-induced phase coefficients. Numerical results demonstrate notable performance in the presence of unknown position deviations.

Shear-horizontal surface acoustic wave (SH-SAW) filters have shown strong potential for low-loss, compact, GHz-frequency RF front ends. In this work, we demonstrate a high-performance SH-SAW filter design at 4.35 GHz utilizing 42°Y-cut thin-film lithium tantalate (LiTaO3) on a SiO2/Si platform. Despite the limitations of thin aluminum metallization and its associated ohmic losses, we show that implementing a Bartlett window apodization technique, primarily intended for in-band spurious-mode suppression, yields a significantly improved quality factor (Q) of 1,522 from 688 in conventional interdigitated SH-SAW resonators. This enhancement enables a third-order ladder filter at 4.3 GHz with an insertion loss of 1.59 dB, compared with 1.65 dB for a conventional SH-SAW filter. In addition, our filter with apodized resonator designs achieves a 3 dB fractional bandwidth (FBW) of 3.24% and out-of-band rejection exceeding 14 dB, all within a compact footprint of 0.4 mm2. These results suggest that apodized thin-film LiTaO3 designs are highly promising for low-loss, miniaturized, cost-effective radio frequency acoustic solutions in next-generation communication and sensing applications.

Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent challenges such as motion blur, specular reflections, and illumination instability. Most existing methods are optimized on clean benchmark images and suffer noticeable performance degradation when deployed in authentic surgical scenarios. We propose DepthPolyp, a lightweight and robust segmentation framework based on pseudo-depth-guided multi-task learning and efficient feature modulation. The architecture combines hierarchical Ghost factorization for compact feature generation, Interleaved Shuffle Fusion for low-cost cross-scale interaction, and Dynamic Group Gating for adaptive group-wise feature weighting. Extensive experiments demonstrate that DepthPolyp achieves strong cross-dataset generalization when trained on degraded data and evaluated on both clean and noisy target domains, consistently outperforming lightweight baselines and remaining competitive with substantially larger models. In real surgical video evaluation on PolypGen, DepthPolyp achieves better segmentation performance than models up to $20\times$ larger while preserving real-time inference speed. With only 3.57M parameters and 0.86 GMACs, the proposed method runs at over 180 FPS on mobile devices, making it well suited for real-time deployment in resource-constrained clinical environments. Code and pretrained weights are available at: https://github.com/ReaganWu/DepthPolyp/

The groundbreaking metric age of information (AoI) has been introduced to measure information freshness in communication networks. As transformational as it is, AoI metric falls short in some applications, such as remote monitoring, since it is a semantic-agnostic metric which does not consider the dynamics of the random process. There is a need to quantify the performance of a remote estimator via a metric that combines freshness and semantic aspects. To this end, in this paper, we introduce a novel metric coined age of staleness (AoS) that measures when the last time that the current estimation was correct. First, we analyze a simple scenario where an $n$-ary symmetric Markov source is observed by a monitor via a constant sampling rate, obtain a closed-form expression for the AoS, and show that it is a monotonically decreasing function of the sampling rate. Next, we consider multiple distinct Markov sources, and formulate an optimization problem, where the remote monitor allocates the total sampling rate to tracking the sources. Although the optimization problem is non-convex, its structure is suitable for obtaining a near-optimal solution using the polyblock algorithm, which leverages the monotonicity of the objective function. While the new AoS metric could be applicable in many scenarios, we believe it is particularly well-suited for a digital twin network (DTN) where multiple physical objects (POs) are monitored with a total sampling rate constraint to maintain a digital representation of them, namely, their digital twin (DT).

Wireless networks support multi-user (MU) communication with multiple-input multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) technologies. In the joint MU-MIMO-OFDMA-enabled transmission mode, network throughput can be significantly increased by effectively utilizing the multi-channel resources to schedule numerous wireless users/stations (STAs) simultaneously. In this paper, we study ways to optimize the user scheduling and resource allocation (SRA) for the UL scheduled access (UL-SA) of a joint MU-MIMO-OFDMA-enabled wireless local area network (WLAN). In particular, we propose a multi-agent (MA) framework that utilizes an openly available pretrained small/medium-sized Language Model (xLM) to perform SRA for the UL-SA. To facilitate autonomous SRA using our proposed technique, we introduce the AI-assisted Wireless Systems Engineering and Research (WiSER) platform. We evaluate the performance of MAxLM-optimized SRA for network scenarios with a varying number of STAs and antenna settings on the WLAN Access Point. Numerical results confirm that our proposed technique achieves higher UL-SA throughput than the benchmark techniques.

SiFo, a wireless foundation model-based framework, is proposed for low-overhead site-specific channel state information (CSI) feedback. In 3GPP NR, Type-II feedback provides an expressive codebook-based CSI representation, but it requires substantial reference-signal overhead, UE-side search, and feedback. Learning-based site-specific feedback can reduce these online costs while retaining high-quality subspace representation by exploiting deployment-dependent propagation structure. However, existing site-specific designs typically train a dedicated neural network for each new site, which limits scalability when the number of deployments is large. SiFo addresses this scalability issue by pretraining a CSI feedback model across source sites and adapting it to a target site through lightweight calibration. A small set of target-site users reports low-dimensional reference signal received power (RSRP) fingerprints, and their full-CSI-based subspace labels are stored as calibration memory. During online operation, a served user is matched to calibrated users through the same SSB probing and RSRP reporting procedure, so nearby calibration samples provide site-specific subspace guidance without updating model parameters. SiFo therefore transfers common propagation knowledge while retaining local adaptation. Numerical results across ten city scenarios demonstrate that SiFo (i) achieves higher CSI-capture efficiency than separately trained site-specific learning baselines under the same target-site labeled budget, (ii) approaches the high-overhead 3GPP NR Type-II feedback reference using only RSRP measurements collected during online SSB probing, and (iii) converts the high CSI-capture efficiency and low overhead into effective spectral efficiency improvement under limited target-site data.

Terahertz communication offers vast bandwidth for high-speed transmission in the 6G networks but faces severe blockage challenges in the near-field region due to large antenna arrays. To overcome the limitation that near-field focused beams are susceptible to obstacles, wavefront engineering is leveraged to generate an Airy beam that propagates along a parabolic trajectory to circumvent blockages. In this paper, we consider the reconfigurable holographic surface (RHS) as a potential solution for such precise wavefront engineering owing to its compact radiation element spacing being much smaller than half-wavelength. We reveal that the adjustable effective aperture of the RHS allows the parabolic offset to be located within the antenna aperture, which enhances the freedom in designing Airy beam trajectories. An analog beamforming method, named the holographic Airy beamforming scheme based on amplitude control, is then proposed to generate the curved beam that propagates along the desired trajectory. To maximize the received power of a blocked user, we develop a geometry-based trajectory optimization algorithm. Simulation results validate that, compared to traditional phase-controlled arrays with analog beamforming, the RHS can leverage its adjustable effective aperture to improve the received power of the blocked user by over 10 dB.

Fast and Relaxed Vector Fitting (FRVF) is a frequency-domain system identification approach that has been widely adopted in electrical system modelling, while its application to mechanical systems has remained relatively unexplored. In this work, FRVF is reformulated for the identification of structural modal parameters of an aircraft based on Ground Vibration Test (GVT) data within a Multi-Input Multi-Output (MIMO) framework. The proposed procedure consists of three stages: (i) rational approximation of frequency response functions via an enhanced input-stacking strategy, (ii) identification of system poles from the resulting rational model, and (iii) estimation of modal parameters from the extracted poles and associated residues. The methodology is first numerically validated on a MIMO beam model, with particular emphasis on accuracy and robustness under increasing measurement noise. Subsequently, experimental validation is conducted using GVT data from the BAE Systems Hawk T1A aircraft. The results obtained demonstrate a level of performance comparable to that achieved by existing methods. Overall, the extended MIMO formulation of FRVF exhibits high accuracy and strong robustness to measurement noise, highlighting its suitability for application in GVT-based modal analysis.

We consider the optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, where the propagation environment design achieved scattering matrix optimization is complemented by developing an efficient base station (BS) beamforming (BF) scheme that effectively exploits the latter ``engineered'' channel. In particular, we describe a fractional programming (FP) method, which based on the equivalent channel incorporating a reciprocal BD-RIS (RBD-RIS) parameterized by existing scattering matrix design methods, yielding the correspondingly optimized multiple-input multiple-output (MIMO) BF weights. The proposed approach decomposes the transmit (TX) beamformer into multiple sum-rate maximization (SRM) sub-beamformers, each satisfying an independent power-constraint, such that distributed MIMO-BF scenarios can be optimally handled. Although the proposed SRM-MIMO-BF framework is independent of the specific scattering matrix design, extending the BD-RIS-aided system model to the CF-mMIMO setting requires the design of a corresponding beamforming matrix. In this context, this work investigates the impact of beamforming in reconfigurable intelligent surface (RIS)-aided systems. Simulation results demonstrate that the proposed method for designing the MIMO-BF weights, when combined with the previously developed design of reciprocal BD-RIS (RBD-RIS) scattering matrices, outperforms existing BD-RIS-aided state-of-the-art (SotA) schemes employing existing MIMO-BF techniques, indicating that the whole contribution is more than the sum of the parts.

Inferring latent dynamics from multivariate time-series defined over topological cell complexes is crucial for capturing the complex, higher-order interactions inherent in real-world systems such as in water, sensor, and transportation networks. However, reconstructing these latent states is challenging because the signals are coupled across higher-order topologies, while high dimensionality, nonlinear observations, and unknown structures increase the difficulty. To address this, we propose a topology-aware state space framework derived from stochastic partial differential equations on cell complexes. State evolution follows heat-like topological diffusion, with perturbations propagating along boundary operators. Under partial observability, we model observations using a cell complex convolution of latent states coupled with a nonlinear mapping. We perform recursive state estimation via an Extended Kalman Filter, simultaneously learning model parameters and uncertainties through an online Expectation-Maximization algorithm. Finally, for scenarios where only lower-order topological structure is known, e.g., nodes and edges, as in critical infrastructure networks, we introduce a heuristic cell identification algorithm to explicitly infer the second-order cell structures. Validations on synthetic and real datasets from water, sensor and transportation networks demonstrate that our approach yields reliable estimates under partial observability and successfully recovers the underlying topological structures.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided near-field integrated sensing, communication, and power transfer (ISCPT) framework is proposed. We formulate a robust harvested power maximization problem under imperfect cascaded channel state information (CSI), with constraints on required user rate, eavesdropper tolerable rate, and minimum sensing beampattern gain. To address this non-convex problem, we adopt alternating optimization (AO). First, we approximate the semi-infinite inequality constraints using the S-procedure and obtain rank-one active beamforming via sequential rank-one constraint relaxation (SROCR); then we update the passive STAR-RIS coefficients with a penalty-based scheme refined by successive convex approximation (SCA). Simulations in the near field demonstrate notable gains in harvested power while meeting secrecy and beampattern targets, outperforming conventional baselines.

Future wireless networks are moving toward autonomous service operation, where network control and resource management need to respond to time-varying radio conditions and evolving service objectives. To address this shift, this article develops an agent-native wireless communication framework that characterizes the interplay between agent intelligence and communication systems. In this framework, the coupling is organized around \emph{agents for communications} and \emph{communications for agents}. For agent-native operation, the architecture is organized around deployable computing infrastructure, programmable open radio access network (O-RAN) software, and controllable communication interfaces. Based on this architecture, \emph{agents for communications} addresses the use of agents in communication-system design and operation, including agent-generated communication software and agent-driven adaptive wireless optimization. On the other side, \emph{communications for agents} addresses wireless service support for agent operation, including network-supported single-agent loops and network-assisted multi-agent coordination. Finally, it outlines promising research directions for measurable, safe, and interoperable deployment of agent-native wireless communications.

Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

The Alliance for Open Media (AOMedia) has developed the AV2 video coding standard to supersede AV1, aiming for substantial compression efficiency gains across diverse media applications. This paper details the quality and performance evaluation methodology defined in the AV2 Common Test Conditions (CTC), which introduces new evaluation methods and content, including convex-hull-based adaptive streaming (AS) configuration, user-generated content (UGC), and extended chroma formats. We present the coding gains of the AV2 (v13.0) against the AV1 baseline. Experimental results show that AV2 achieves significant Bjøntegaard-Delta Rate (BD-rate) reductions of 29.81\% and 33.79\% for PSNR-YUV and VMAF, respectively, under random access configuration, validating the efficiency of AV2 for next-generation streaming applications.

Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely on conservative bounds rather than point estimates. Predictor-level intervals (e.g., from quantile regression, conformal prediction, variance networks, or Bayesian models) generally do not compose: adding two per-variable intervals need not yield a valid interval for their sum or preserve coverage. In aviation, Gaussian overbounding replaces complex error distributions with a conservative Gaussian whose tails dominate the truth, so conservatism propagates through linear operations. Yet classical overbounds are global, often overly conservative, and hard to adapt to feature-conditioned errors. We propose a unified learning framework that trains neural networks to produce context-aware Gaussian overbounds--mean and scale--with provable conservatism on a finite quantile grid and, under three explicit regularity assumptions, continuous-tail conservatism on a certified interval. Our overbounding loss enforces conservativeness at selected quantiles while penalizing distributional distance with a Wasserstein-style term. The learned bounds support conservative linear-combination and convolution analysis on the enforced grid, and on the certified interval when assumptions hold, while being less redundant than traditional methods. We provide a scoped analysis of discrete-to-continuous conservatism and compact-domain objective regularity, and validate on synthetic data and real-world datasets, including multipath, ionospheric, and tropospheric residual errors. Across these settings, the method yields tighter bounds while maintaining conservatism on the enforced grid and in experiments. The framework is modality-agnostic and applicable to learning systems that require conservative, feature-conditioned uncertainty estimates in dynamic environments.

Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However, the scarcity of annotated data poses a significant challenge for training deep learning models. Conventional semi-supervised semantic segmentation (SemiSeg) methods primarily focus on consistency from unlabeled data, underutilizing the information exchange possible between labeled and unlabeled sets. To address this, we introduce CardioMix, a framework built on a bidirectional CutMix strategy guided by cardiac patterns for ECG segmentation. This approach enriches the labeled set with realistic variations from unlabeled data while simultaneously applying stronger supervisory signals to the unlabeled set, as the cardiac pattern-guided mixing ensures all augmented samples remain physiologically meaningful. Our framework is designed as a plug-and-play module, demonstrating high compatibility with various SemiSeg algorithms. Extensive experiments on SemiSegECG, a public multi-dataset benchmark for ECG delineation, demonstrate that CardioMix consistently outperforms existing CutMix-based fusion strategies across diverse datasets and labeled ratios as a plug-and-play module compatible with various SemiSeg algorithms.

Under the trend of multi-waveform coexistence in 6G IoT, intelligent receivers must first identify physical-layer waveform types before performing correct demodulation and resource scheduling. However, existing signal identification research largely focuses on symbol-level modulation classification. Research directly targeting physical-layer waveform types (e.g., OFDM, OTFS, LoRa) is not only extremely scarce but also heavily reliant on deep neural networks and complex time-frequency transforms, making deployment on resource-constrained terminals difficult. Symbol modulation classification methods themselves cannot circumvent the prerequisite of ``waveform identification first.'' To address this dual gap, we propose an ultra-lightweight waveform classification framework based on time-frequency multidimensional features with a cooperative Z-test tree (ZTree). The framework employs low-complexity time-domain feature extraction, and the classification backend adopts a ZTree optimized by Z-statistical testing, which uses hypothesis testing confidence to automatically control decision tree splitting and size, ensuring efficient execution on resource-limited processors. Tested on ten 6G candidate waveforms including OFDM, OTFS, DSSS, LoRa, and NB-IoT, the method achieves 99.5\% average accuracy under AWGN and 87.4\% under TDL-C multipath channels, with main confusion between OTFS and LoRa. Implemented in C on an x86 platform, single inference latency is under 4~ms. To the best of our knowledge, this is the first work achieving real-time recognition of ten IoT waveform types. Future work will target deployment acceleration on embedded MCUs. Code and dataset are open-sourced at: https://github.com/Einstein-sworder/IoT-wave.

Radio Frequency Fingerprinting (RFF) using deep learning has gained attention as a complementary approach to cryptographic authentication, offering resistance to spoofing, replay attacks, and key leakage. While most RFF approaches rely on In-Phase and Quadrature (IQ) samples, Channel State Information (CSI) has emerged as a more accessible alternative, enabling device authentication through physical-layer characteristics. In this work, we propose ContraCSI, a CSI-based contrastive learning framework for RFF using low-cost ESP32 devices. We investigate multiple encoder backbones, including a Vision Transformer (ViT), a lightweight 3D-CNN (Lite3D-CNN), and R3D18, to learn joint CSI and device-ID embeddings for transmitter authentication. For closed-set identification, the ViT variants achieve the best overall performance. We further study open-set authentication by applying a Geometric Entropy Minimization (GEM)-based anomaly score and sequential CUSUM (Cumulative Sum) test on embeddings learned by Lite3D-CNN-Contra, enabling rejection of unseen or non-enrolled transmitters rather than forcing a closed-set label. To evaluate robustness in highly dynamic and crowded indoor environments with human motion, multipath fading, and varying device orientations and distances, we conduct extensive experiments in a real-world setting. Our results demonstrate high authentication accuracy, strong generalization in non-ideal conditions, and effective rejection of unknown transmitters. Additionally, we explore CSI-based indoor localization via trilateration, illustrating the potential for integrated authentication and localization in practical indoor deployments.

Radio maps provide a foundational data layer for wireless digital twins, and federated learning offers a natural framework for their distributed construction without centralizing raw radio environment data. However, the exchanged client model updates may still leak transmitter-location information, even when the underlying measurement data are never shared. Existing noise-based privacy defenses inject perturbation either uniformly across all uploaded coordinates or according to a fixed static rule, thereby ignoring the architecture-specific structure of this leakage. This paper proposes a budget-constrained adaptive noise allocation mechanism that redistributes a fixed perturbation budget across transmitter-sensitive upload groups identified from the two-stage RadioUNet architecture. The proposed method uses low-dimensional upload statistics to dynamically adjust group-wise noise scales and is integrated locally before client upload transmission. We evaluate the framework on a federated radio map learning task under a unified noise multiplier, comparing it against uniform and structure-aware baselines using reconstruction mean squared error and transmitter localization error as metrics. Results show that adaptive allocation achieves the strongest privacy protection while maintaining the best reconstruction quality among all noise-based defenses under a matched perturbation budget.

One central theme in quantum photonics is tailoring the interactions between atoms/spins and their electromagnetic (EM) environments. Considerable effort has focused on engineering spontaneous emission by shaping EM environments, known as the Purcell effect. However, photonic environment control of pure dephasing, which is a complementary paradigm of non-unitary atom/spin couplings with EM environments, remains largely unexplored. Here, we introduce a nanophotonic approach to modify qubit pure dephasing dynamics. Unlike Purcell engineering that tailors photonic environments at qubit resonance frequencies (typically optical/near-infrared), we develop ultra-subwavelength spin noise metasurfaces for efficient broadband control of low-frequency (e.g., $\sim$MHz) photonic environments far off-resonant with atoms/spins for dephasing engineering. We experimentally demonstrate our approach using lithographically defined CoFeB metasurfaces and shallow nitrogen-vacancy (NV) centers in diamond. Instead of modified spontaneous emission, we observe modified NV pure dephasing dynamics near different spin noise metasurfaces. We further isolate metasurface-controlled dephasing from other dephasing mechanisms (e.g., spin bath) by measuring the NV ensemble dephasing noise spectrum with dynamical decoupling spectral decomposition techniques. Our results establish a new frontier in engineering quantum light-matter interactions with nanophotonic structures.

We investigate the trajectory-level dynamics of a double quantum dot system using the newly developed formalism of stochastic excursions. This approach extends full counting statistics by enabling a filtering of complex trajectories into sub-trajectories, which provide access to the intricate correlations between thermodynamic currents and excursion times. Counting observables are the main object of study in the stochastic excursion framework. Those are defined as a linear combination of transition counts multiplied by their assigned weights within one excursion. For three main counting observables -- charge current, dynamical activity, and entropy production -- we compute averages and noise contributions and show how they provide insights into the operation of the double quantum dot system. At the trajectory level, we analyze outcome distributions for transport and connect the results with trade-offs between successful and unsuccessful events that shape overall performance. We further introduce state observables, which depend on the state visited rather than the transition itself, and discuss the population of the two dots, as well as their correlations. Finally, we discuss thermodynamics of precision through thermo-kinetic uncertainty relations, showing how current precision in different regimes is fundamentally constrained either by entropy production or by dynamical activity. Altogether, our work is a case study that highlights the utility of the excursion framework as a toolkit to analyze many quantities of interest and to uncover the structure of nonequilibrium fluctuations. Moreover, it also suggests new avenues for refining uncertainty relations and understanding transport in mesoscopic systems.

Pauli Correlation Encoding (PCE) compresses $m$ binary variables onto $n=O(m^{1/k})$ qubits by mapping them to commuting Pauli correlators, but its continuous expectation values must be decoded into feasible binary solutions, a challenge for dense-constraint problems. We apply PCE to mRNA secondary-structure prediction, formulated as a densely constrained QUBO, and train with a QUBO-space sigmoid loss thatpreserves the QUBO penalty structure. For decoding, we introduce the Problem-Aware Guided Decoder (PAGD), which scores candidate variable commitments by combining marginal QUBO energy reduction with a trained expectation-value prior and constraint-aware feasibility pruning. On six benchmark mRNA sequences (30-60 nt, 50-240 variables, 7-14 qubits), PAGD with 100 restarts achieves 75-100 percent near-optimal recovery, defined as $P(\mathrm{gap}<1\%)$, for sequences up to 152 variables, compared with 0-30 percent for a sign-rounding plus local-search baseline. On the 240-variable instance, trained PAGD reaches 50 percent $P(\mathrm{gap}<1\%)$ at 200 restarts, outperforming untrained-circuit and random-expectation-value controls. Hardware-scale tests extend the pipeline to three 102-105 nt instances (694-745 variables, 172,000-193,000 pair constraints, 23 qubits) on IBM Heron processors. The circuits transpile SWAP-free into 480 native two-qubit gates at depth 256, and PAGD decoded gaps on QPU runs match or beat simulator means for all three instances, including exact CPLEX-optimum recovery for one sequence. These results show that PCE-trained priors can survive deployment to noisy superconducting hardware at biologically relevant scale.

Discrete Gaussian Sampling on lattices is a fundamental problem in lattice-based cryptography. It appears both in basic cryptographic primitives such as digital signatures and as an important cryptanalysis building block for solving hard lattice problems. In this paper, we show a quantum algorithm based on the quantum rejection sampling technique whose complexity is asymptotically quadratically faster than its classical counterpart in [Wang & Ling, IEEE Trans. Inf. Theory 2019]. Our sampler outputs a quantum state which can either be measured to get the desired distribution or be used directly as such in other quantum algorithms. By doing so, we derive two versions of quantum dual attacks that improve upon the previous ones in [Pouly & Shen, EUROCRYPT 2024]. The two versions are incomparable, each having distinct advantages (speed vs memory requirement). The second version is particularly interesting as it requires only polynomial classical and quantum memory, excluding the classical memory used in the preprocessing step of the Discrete Gaussian sampler. Our quantum Discrete Gaussian sampler can also be used to speed up the algorithm for solving the Short Integer Solution problem, in any norm, of [Bollauf, Pouly & Shen, ePrint 2026/225].

Spectral-siren cosmology constrains the Hubble constant $H_0$ using gravitational-wave observations of compact-binary coalescences. The method combines luminosity distances inferred from the waveform with redshift information statistically encoded in population features of the source-frame mass spectrum. Because the detector measures redshifted masses, structure in the intrinsic mass distribution acts as an internal ``ruler'', making the inference sensitive to assumptions about the population model. In particular, redshift evolution of the mass spectrum is widely discussed as a potential systematic for $H_0$ measurements. We revisit spectral-siren constraints with the GWTC-4.0 binary black hole catalog, explicitly allowing the main mass scales of a standard parametric mass model to evolve with redshift. We find no compelling evidence for evolution at current sensitivity. Allowing evolution produces a modest, non--statistically--significant shift of the $H_0$ posterior toward lower values, which we interpret with targeted posterior and event-level diagnostics. Importantly, the associated systematic uncertainty is subdominant to that induced by alternative redshift-independent descriptions of the mass spectrum, such as the number of spectral features and the functional form used to model them. Our results indicate that, at current sensitivity, spectral-siren constraints on $H_0$ are robust to redshift evolution of the mass spectrum within the flexibility explored here. Using injection studies, we show that this mild $H_0$ shift is reproduced when a non-evolving underlying population is analyzed with an evolving model, consistent with an over-flexible population description at the present signal-to-noise. The sign and magnitude of the shift can, however, depend on detector sensitivity and redshift reach as the population features become increasingly constrained directly by the data.

The specific advance of this work is to propose a mechanism by which superpositions collapse during measurement of the separated subsystems of entangled quantum states. It is shown how the phase that locks together entangled states plays a special role in the measurement of isolated subsystems. This `contextual' phase is installed randomly into the entangled state, and decides the measurement outcomes for the subsystems by directing the collapse of each superposition to a particular classical outcome when a subsystem is measured. The measuring apparatus thus obtains a classical read-out of the quantum correlations embedded in an entangled state. More broadly, these results solidify the theory of measurement of quantum superpositions.

We investigate a non-relativistic realization of the boostless cosmological collider in a scenario where inflationary interactions are mediated by a massive tilted-ghost spectator field. Unlike standard boostless collider constructions, in which the characteristic non-Gaussian signatures are mainly generated by boost-breaking interaction vertices, the dominant effect in the present framework arises directly from the propagation of the spectator modes. Non-relativistic corrections deform the bulk mode functions, inducing a tilt that modifies the in-in correlators and generates a distinctive collider signal. The resulting squeezed-limit non-Gaussianity reproduces the qualitative structure of boostless cosmological-collider signals while originating from a fundamentally different dynamical mechanism. A central feature of the construction is the emergence of an effective chemical-potential-like parameter that controls the relative weight of the two late-time oscillatory branches. However, the tilted-ghost mode exhibits distinctive dynamical features and does not correspond to a conventional chemical-potential deformation. Depending on the sign of the tilt, the corresponding non-Gaussian signal can be either enhanced or suppressed. We show that the tilted-ghost scenario provides a simple effective framework in which boostless-collider phenomenology and chemical-potential-like branch asymmetries arise naturally from non-relativistic propagation effects.

Most of the laws of Nature involve derivatives up to the second order. Ostrogradski was the first to seek a formulation of the equations of higher-order derivatives. He extended Hamilton's equations by considering Lagrangians that depend on higher-order derivatives of generalized coordinates. The Hamilton-Ostrogradski formulation served as the basis for later studies with higher-order derivatives. However, the Hamilton-Ostrogradski formalism is rarely discussed in textbooks or the pedagogical literature. This motivated us to show how the Hamilton-Ostrogradski formalism can be applied it to the Pais-Uhlenbeck oscillator. We hope that the approach presented in this work can serve as a basis for discussion in advanced classical mechanics courses.

Weakly almost i.i.d. quantum sources are sequences of multipartite states whose fixed-size marginals converge, on average, to tensor powers of a reference state, while allowing arbitrary global correlations and entanglement. We establish two concentration principles for such sources: a noncommutative weak law of large numbers for empirical observables, and a universal entropy-concentration principle showing asymptotic concentration on subspaces of exponential dimension governed by the von Neumann entropy of the reference state. These concentration principles provide a unified and conceptually transparent approach to several information-theoretic applications beyond the i.i.d. setting, including direct proofs of universal compression within classes of weakly almost i.i.d. sources sharing a common reference state, asymmetric quantum hypothesis-testing bounds, concentration results for macroscopic observables in quantum many-body systems including generalized Gibbs ensembles and for repeated local measurement statistics, as well as bounds on smooth- and spectral entropy quantities.

Digital quantum simulation offers a promising route for studying quantum dynamics, but efficient operator representations and circuit depth remain key challenges for near-term hardware. We investigate one-dimensional wave packet dynamics using a grid-based encoding of the wave function onto qubit registers. Time evolution is implemented via split-operator approach, with kinetic energy operator applied using Quantum Fourier Transform (QFT) with polynomial scaling and potential energy operator expressed through commuting Pauli-Z gates, improving accuracy and enabling incorporation of arbitrary discretized potentials. While the full Pauli decomposition of Hamiltonian scales exponentially as O(4^n ), the present approach reduces the operator scaling to O(2^n) for n qubits. We benchmark this approach on classical simulators and quantum hardware (IBM Quantum and IonQ) for two- to five-qubit implementations. For two- and three-qubit cases, all platforms qualitatively reproduce the benchmarked dynamics; at larger qubit counts, the IBM results deviate more strongly, whereas IonQ remains closer to the benchmark.

Quantum Key Distribution (QKD) provides secure keys for classical communications through one-time-pad (OTP) encryption with physical-law security. Advanced PON-based Classical Access Networks (CANs) support up to 256 users with a total rate of 10 Gbps (10-Gbps @ 256-users). The equivalent rate demand of OTP encryption requires QKD Access Networks (QANs) to reach comparable performance, yet state-of-the-art PON-based QANs remain far from this standard. To address this gap, we propose a passive Thermal-State QAN (TS-QAN) distributing polychromatic quantum randomness from a single thermal source and supporting 304 users with an aggregate secret key rate (SKR) of 13 Gbps (13-Gbps @ 304-users). This performance is enabled by three features. First, broadband thermal states with Bose-Einstein statistics can be represented, through the Glauber-Sudarshan representation, as high-bandwidth Gaussian coherent-state ensembles across frequency modes, eliminating many active modulators and quantum random number generators (QRNGs). Second, Electro-Optic (EO) comb beacons provide time-varying polychromatic phase tracking, so each frequency-mode thermal signal can be coherently measured with a Local Local Oscillator (LLO) aided by its beacon, without large-scale phase-locking networks. Third, state broadcasting allows each user to obtain independent final keys via reverse reconciliation after accounting for residual broadcast-induced correlations, expanding network capacity with small SKR losses. Experimentally, we verify a 13-Gbps @ 304-users TS-QAN using Continuous-Variable QKD (CV-QKD) under covariance-matrix-based network security analysis including multimode Holevo leakage and broadcast correlations. This work meets the SKR and capacity demands from CAN to QAN: 13-Gbps @ 304-users satisfies the 10-Gbps @ 256-users benchmark and provides a scalable solution for modern telecommunication systems.

In this paper we propose a novel unified cosmological model that connects a pre-inflationary epoch, starting at the Planckian time, with the onset of inflation within a single scalar-field framework. The pre-inflationary phase is characterized by a decelerated expansion with an increasing comoving Hubble horizon, followed by a gradually transition to an accelerated inflationary regime. This early dynamics leads to a modified causal structure that naturally accounts for the suppression of large-angle $(θ\gtrsim 60^\circ)$ correlations in the cosmic microwave background (CMB) reported by the satellite PLANCK. We study the quantum fluctuations of the scalar field using the Mukhanov-Sasaki formalism and a canonical quantization procedure based on energy minimization. We find that the vacuum state is well-defined only for sub-horizon modes at the onset of inflation, which induces a natural cutoff in the primordial power spectrum. The resulting spectrum exhibits a suppression at large scales while remaining nearly scale-invariant at small scales. In the appropriate limit, the model recovers the standard de Sitter result, in agreement with current observational constraints. These results highlight the relevance of pre-inflationary dynamics for addressing large-scale anomalies within a consistent inflationary framework.

We develop a pivot-shifted Carleman linearization framework for quantum algorithms solving quadratic nonlinear ordinary differential equations. By shifting the dynamics by a pivot state prior to Carleman lifting, and combining this with a Lyapunov transform and rescaling, we enlarge the class of nonlinear systems that can be efficiently simulated on quantum computers. For systems that exhibit stability in the shifted coordinates, we establish long time convergence of the truncated Carleman embedding. We prove that the truncation order scales only logarithmically with the simulation time and target precision, and we derive end-to-end quantum query complexity bounds for preparing a state proportional to the final solution. By introducing a modified nonlinearity condition, this framework entirely removes the conventional lower bound requirement on the initial condition. For more general systems that remain unstable after shifting, we provide short time convergence guarantees that are similarly free from the initial condition constraints. Numerical experiments on the logistic and the Lotka-Volterra equations demonstrate that an appropriate pivot choice improves stability and accuracy, and yields exponential error decay with truncation order. These results show that pivot shifting provides a practical and theoretically justified route for extending Carleman-based quantum algorithms to a broader class of nonlinear dynamical systems.

Determining the dynamics of interacting integrable many-particle quantum systems at finite times after homogeneous quantum quenches is a long-standing challenge. We present a Monte Carlo sampling scheme that numerically evaluates the Lehmann representation for time-dependent expectation values of local operators, allowing us to access system sizes and times significantly beyond the reach of existing methods. The approach accommodates both the full Lehmann sum and the Quench Action formalism. We benchmark against exact results for non-interacting lattice and continuum models and short-time results at weak interactions, finding excellent agreement. We apply the method to quantum quenches from a Bose-Einstein condensate in the repulsive Lieb-Liniger model and determine the time evolution of the order parameter for a wide range of interaction strengths. We discuss the emergence of a "sign problem" for more general dynamical correlators and setups.

The article deals with the role of gravitational radiation energy in the large-scale dynamics of the universe. Motivated by the observed accelerated expansion, we investigate whether gravitational energy, treated as a well-defined physical quantity within the teleparallel equivalent of general relativity, contributes to cosmological acceleration through its associated energy flux. Using radiative space-times described by the Bondi--Sachs framework, we analyze the total gravitational energy and the corresponding energy flux evaluated in asymptotic regions. Particular emphasis is placed on the cumulative character of gravitational radiation over long time scales and on the fact that gravitational energy in this formulation is not positively definite. The present analysis provides a consistent theoretical basis for assessing the relevance of gravitational radiation energy and its flux in cosmological contexts.

We study a time-dependent non-Hermitian extension of the Schütte-Da~Providência spin-boson Hamiltonian with complex couplings. A time-dependent Dyson map containing a squeezing transformation maps the model, in an admissible bounded regime, to a Hermitian Hamiltonian with real instantaneous energy spectrum. The squeezing contribution generates a dilatation term allowing the Hermitian partner to be interpreted as a fixed-domain representation of a system with moving boundaries. While the fixed-boundary Hermitian model conserves $Q=N-S_0$ and forbids transitions between sectors differing by two bosonic quanta, the boundary motion opens such channels. For closed boundary protocols with constant background parameters the first-order integrated transition amplitude vanishes, reflecting the unitary nature of constant squeezing. Nontrivial transition control arises when the non-Hermitian parameter varies during the boundary motion, changing the dressed basis and allowing boundary-induced transitions to be suppressed or enhanced by coherent interference.

Many approaches to quantum gravity require replacing the traditional black hole geometry with an Exotic Compact Object (ECO), which has a large but not infinite redshift at its surface. We argue that near the ECO surface, the vacuum Einstein equations imply a metric that is chaotic, with increasingly large oscillations as we approach the surface. This behavior is analogous to the `cosmic billiards' found in the BKL analysis of cosmology near the big bang. For the ECO, some of the potential walls of this billiards change sign to become `cliffs', resulting in a runaway behavior where some compact directions squeeze to zero size. In string theory such squeezing yields a natural continuation to the interior geometry of fuzzballs, where compact directions collapse to create monopoles.

We present the characterization of a photon-number-resolving detection chain based on Silicon photomultipliers (SiPM) coupled to a 14 bit, 1 Gs\s digital acquisition system embedding an FPGA-based signal processing pipeline that performs real-time baseline subtraction, digital deconvolution, and charge integration. Three SiPM models manufactured by Hamamatsu are tested and compared in the mesoscopic intensity regime using both classical coherent states and quantum twin-beam states, enabling a systematic investigation of the effects of pixel pitch, pile-up, and photon detection efficiency on the detector performance.

We present PEPSKit.jl, a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS). PEPSKit.jl builds on the TensorKit.jl package for tensor computations and provides high-level algorithms for iPEPS simulations that support both Abelian and non-Abelian symmetries, as well as fermionic systems. This work gives an overview of the main package features, which include support for ground-state, time-evolution, and finite-temperature simulations in systems with different physical symmetries and lattice geometries. These capabilities are illustrated through various examples and technical benchmarks.

TeV string-mass-scale strings have been excluded experimentally at colliders, as their effects have not been observed at the Large Hadron Collider (CERN). On the other hand, higher-scale string theory, with mass scales typically close to the Planck scale, is often regarded as experimentally inaccessible due to the enormous energies required for direct tests, and far beyond the reach of present or foreseeable particle accelerators. Nevertheless, the early Universe may provide an indirect observational window for high-string scale through imprints left on the Cosmic Microwave Background (CMB). In this work, building on previous studies, we reexamine temperature and polarization angular correlations as probes of the geometry and topology of the pre-inflationary Universe. We focus in particular on two-point correlation functions at large angular scales, where signatures of nontrivial spatial topology may survive as relics of primordial physics. We investigate the observational consequences of toroidal compactification and analyze their impact on the primordial power spectrum of the CMB provided by the Planck satellite. Within the current experimental and theoretical uncertainties, we identify a possible indication closely related to spatial-parity breaking, consistent with the presence of six spatial extra dimensions in the early Universe, compactified at the GUT epoch before the start of inflation. Finally, we extend our framework to B-mode polarization, highlighting its potential as a sensitive probe in forthcoming ground-based and space-borne experiments with unprecedented precision.

Using a recursive solution of the Einstein equations, we consider the perturbative expansion of the metric corresponding to a Kerr black hole. Because the metric is a function of two parameters, Newton's constant G and the Kerr spin parameter a, the perturbation theory naturally becomes a double expansion. In harmonic gauge the recursion relations can be solved to arbitrarily high orders in these two expansion parameters but to re-sum the series into the closed-form harmonic gauge metric requires the introduction of terms that are redundant and correspond to the addition of harmonic functions to the coordinates. Issues related to dimensional regularization of Fourier transforms are explained in detail.

In quantum thermodynamics, the decomposition of energy exchanges into heat and work remains an open problem beyond weak-coupling and slow-driving regimes. Recent formulations have shown that quantum coherence introduces additional energy contributions whose thermodynamic interpretation is still under debate, raising fundamental questions about the structure of the quantum first law. In this work, we investigate this problem through a time-dependent perturbative framework applied to the first law of quantum thermodynamics. By expanding the thermodynamic quantities up to second order, we derive explicit perturbative corrections for work, heat, and coherence contributions. Our results show that the coherence term can be consistently decomposed into coherent heat and coherent work, demonstrating that quantum coherence does not require the introduction of an independent energetic contribution beyond heat and work. The formalism resolves inconsistencies associated with previous formulations of the quantum first law, including the interpretation of coherence contributions and their connection with entropy fluxes. At second order, the perturbative corrections become directly connected to transition rates governed by Fermi's golden rule, establishing a bridge between microscopic quantum transitions and macroscopic thermodynamic quantities. These results provide a physically transparent framework to investigate coherence-driven thermodynamic processes and offer new perspectives for the analysis of driven quantum systems and nonequilibrium quantum technologies.

Can a closed quantum system generate persistent time-crystal-like dynamics without external driving? Within the Bateman dual oscillator framework, we show that the answer is affirmative. We consider a nonrelativistic (2+1)-dimensional system in which spin-induced spatial deformation generates an effective Bateman oscillator structure. After quantization, the system is governed by a time-independent Hermitian Hamiltonian describing coherent coupling between damped and amplified oscillator sectors while preserving the total energy of the global doubled system. Tracing over the amplified sector, we derive an effective non-Markovian reduced dynamics for the observable subsystem. The resulting memory effects sustain persistent oscillations of subsystem observables and generate emergent time-crystal-like temporal ordering without external periodic driving or equilibrium spontaneous symmetry breaking. Since the oscillatory behavior originates from nonequilibrium reduced subsystem dynamics rather than equilibrium expectation values of the full Hamiltonian, the mechanism lies outside the assumptions of conventional no-go theorems for equilibrium time crystals. The same dynamics further exhibits logarithmic-spiral trajectories and self-similar fractal scaling, revealing a direct connection between coherent dissipative dynamics, non-Markovian memory effects, and emergent temporal ordering in a globally unitary quantum system. In this specific sense, "watching the growth" of these self-similar structures corresponds to observing the gradual formation of time-crystal-like ordering.

We study the scattering of charged massive particles impinging on a Reissner-Nordström (RN) black hole immersed in perfect fluid dark matter (PFDM). We obtain an approximation of absorption cross section in the low-frequency regime via the matching method. In the high-frequency regime, we derive the weak-field deflection angle up to second order. The numerical results are in excellent agreement with classical approximation and glory scattering. The effects of dark matter, particle charge, and mass upon scattering and absorption are examined in detail. The results show that as the dark matter parameter $λ$ increases, the absorption cross section of the black hole is strongly suppressed, and its high-frequency limit depends only on the black hole charge $Q$ and $λ$. The scattering cross section also decreases overall. In the superradiant regime, the amplification factor of the PFDM black hole is much larger than that of the RN black hole. Finally, we discuss the behavior of the absorption cross section as $ω/m\rightarrow1$, as well as the scattering cross section at small scattering angles.

Twisted light carries orbital angular momentum (OAM) and can drive excitations of confined, interacting electrons that are dark to uniform dipolar probes. Here we show how this ``beyond-Kohn's-Theorem'' optical channel can become a concrete control primitive for quantum computing. Correlation sectors in few-electron quantum dots -- characterized by the relative angular momentum quantum number -- form a tunable ladder of many-body states that are robust in the limited sense of symmetry-protected selection rules and persistent chiral spectroscopic fingerprints; full topological gap protection requires three or more electrons. A twisted-light pulse with prescribed OAM index and polarization provides fast optical write, read, and scalable addressing of these sectors via the selection rule $Δ|m|=\pm(l+σ)$. In the analytically solvable Calogero ($1/r^2$) interaction limit, both the energy spectrum and the twisted-light matrix elements are closed-form functions of the interaction strength, allowing gate parameters (Rabi frequency, qubit frequency, anharmonicity, and leakage rates) to be written down explicitly. We map these results onto a universal single-qubit gate set, propose a concrete two-qubit entangling mechanism via state-dependent Coulomb coupling between adjacent dots, and identify the dominant decoherence channel (quadrupolar charge noise). A semi-analytic $N=3$ extension using the $1/N$ expansion provides a design-level scaffold for the topological roadmap, including quasihole sector addressing. The central operational message is that twisted light enables WRITE (pulse-create a correlation sector), READ (spectroscopically diagnose correlations), and SCALE (optical addressing via spatial light modulator) in a unified photonic control layer. Throughout, screened and Coulomb interactions preserve the same qualitative chiral fingerprints established in the solvable limit.

Quantum computers are emerging as a promising new technology due to their ability to solve complex problems that exceed the capabilities of classical systems in terms of time. Among various implementations, superconducting qubits have become the leading technology due to their scalability and compatibility with quantum error correction mechanisms. Although time has traditionally been the primary focus, energetic efficiency is becoming an increasingly important consideration, especially with the possibility of a quantum energetic advantage. In this article, the energy consumption of the Semiclassical Quantum Fourier Transform was analyzed on a superconducting quantum computing platform based on cat qubits. Quantum error correction mechanisms were studied and considered in the energy estimations. The results show how the energy consumption scales with the number of qubits and how the most relevant parameters required for qubit stabilization, gate implementation, and error correction codes contribute to the overall energy usage. An optimization method was developed to tune these parameters with the goal of minimizing energy consumption while maintaining qubit fidelities above a given threshold. Additionally, a comparative study with state-of-the-art classical computers indicates a potential quantum energetic advantage for systems with more than 26 qubits, assuming cryogenic systems operating at Carnot efficiency, with this energetic advantage arising before any computational advantage. This behavior persists even when realistic cryogenic systems and control electronics are taken into account.

We investigate a dark energy model driven by a planar domain-wall-like structure with a thickness comparable to, or larger than, the current Hubble radius, focusing on its intrinsic anisotropy and observational viability. Near the centre of the domain wall (DW), the spacetime is anisotropic, with distinct expansion rates parallel and perpendicular to the wall. This anisotropic structure induces direction-dependent cosmic expansion and modifies photon geodesics from cosmological sources, leaving characteristic signatures in cosmological observables. We confront the model with recent observational data. We first compute the anisotropic Cosmic Microwave Background (CMB) temperature multipoles generated by the DW and impose constraints from the Planck 2018 measurements. These constraints severely limit the allowed DW abundance, requiring the DW energy density to be less than $\mathcal{O}(10^{-5})$ of the current critical density in order to suppress the quadrupole contributions. We then perform a Markov Chain Monte Carlo (MCMC) analysis using Type Ia supernova (SNe Ia) data, including the Pantheon+ SH0ES and DESY5 samples, to compare the DW scenario with the standard $Λ$CDM model. We find that although the DW naturally realises anisotropic accelerated expansion, the combined constraints from the CMB and SNe Ia favour the $Λ$CDM limit, in which the DW contribution is negligible, and the universe is effectively isotropic. Our results demonstrate that a Hubble-scale domain wall is tightly constrained by current observations and can only play a subdominant role in the late-time cosmic acceleration.

Motivated by their relevance to the interior of nonrotating black holes, classical and quantum Kantowski-Sachs cosmologies have recently attracted increasing attention. This interest has led to the development of a Hamiltonian formalism for axial and polar perturbations, which can be extended to applications in the exterior region. The formalism provides also a description of the background physical degrees of freedom. Moreover, it allows for the construction of all physical perturbative gauge invariants, which can be arranged into canonical pairs associated with master functions. In this work, we review the basis of this Hamiltonian formalism, putting the emphasis on its foundations and fundamental steps rather than on details of the involved calculations. Our discussion focuses on classical and effective aspects, although we also briefly comment on its natural role in the quantization of perturbed black holes. Adopting this formalism we present a geometric interpretation of Darboux transformations between pairs of master functions, characterizing them as generalized canonical transformations that preserve the Hamiltonian structure of the perturbations as harmonic oscillators subject to certain potentials. This bijective correspondence between such canonical transformations and Darboux transformations, which was recently proved to hold for axial perturbations, is here extended to the case of polar perturbations. In addition, we demonstrate the existence of canonical transformations that, similarly to Darboux transformations, mix axial and polar master functions.

Variational quantum algorithms (VQAs) for combinatorial optimization routinely employ entangling gates as a default design choice, yet the role of entanglement, in its amount and structure, remains poorly understood. This gap is particularly consequential for problems governed by diagonal Hamiltonians, whose ground states are classical product states and therefore require no entanglement in principle, raising the fundamental question of whether and how entangling gates help or hinder the variational search. We investigate this question for MaxCut by introducing two complementary control mechanisms that provide smooth, monotonic control over hardware-efficient ansatz (HEA) entanglement as quantified by the Meyer-Wallach measure $Q$, and by benchmarking against QAOA as a problem-structured reference. Tracking the entanglement trajectory $Q(t)$ throughout VQA training reveals that when the ansatz grants the optimizer indirect control over entanglement through its parameters, it consistently drives entanglement down. In line with this tendency, a fully separable ansatz outperforms all entangled hardware-efficient configurations, establishing a monotonic relationship: less problem-agnostic entanglement yields better performance. In contrast, QAOA, whose entanglement is structurally derived from the problem Hamiltonian, maintains high entanglement yet achieves competitive solution quality, demonstrating that entanglement structure, not merely quantity, determines its utility. These findings suggest that HEAs for diagonal Hamiltonians are inappropriate and that variational approaches to combinatorial optimization should prioritize problem-structured circuit designs.

Primordial Black Holes (PBHs) forming prior to Big Bang Nucleosynthesis evolve during the reheating epoch, an environment dominated by an oscillating inflaton field decaying into a relativistic thermal bath. In this work, we track the complete lifecycle of PBHs within this coupled inflaton-radiation background. Utilizing $α$-attractor E-models, we analytically anchor the reheating initial conditions directly to Cosmic Microwave Background observations. By matching exact scalar field solutions in a Schwarzschild spacetime to the cosmological far-zone, we derive the cycle-averaged mass accretion rate and couple it to the growing radiation bath. We find that this combined accretion induces a highly non-linear enhancement of the final PBH mass. Because the Hawking evaporation timescale scales cubically with mass, PBHs forming near their critical runaway limits experience a massive extension of their lifespans. Surviving deeper into the radiation-dominated era triggers a multi-order-of-magnitude amplification in their emitted Stochastic Gravitational Wave Background (SGWB).

Quantum geometry characterizes the variation of wavefunctions in momentum space through their overlaps and relative phases, providing a general framework for understanding many transport and optical properties. It is generally formulated in terms of interband matrix elements, which, entering the response functions, allow obtaining experimental access to the quantum geometric tensor. Recently, it has been emphasized that quantum geometry can also be interpreted in terms of quantum dipole fluctuations in the ground state driven by interband mixing. Here, we extend this picture to include contributions from many-body collective fluctuations, in which propagators and response vertices are dressed dynamically by the interaction with collective modes. Focusing on the Berry curvature, we show that contributions from collective fluctuations can be experimentally distinguished from bare band-geometric contributions, via specific antisymmetric channels in inelastic scattering spectra. We further identify the non-commutative properties of transverse quantum fluctuations as well as non-local-time interactions as the generators of this dynamical curvature in the susceptibility response.

Based on quantum field theory of linearized gravity, we formulate the Wigner function for right- and left-handed gravitons. By applying the Wigner transformation to the second-order metric perturbations in the graviton energy-momentum tensor obtained from the Einstein-Hilbert action, we demonstrate the emergence of the spin Hall effect of gravitons in curved spacetime. This effect originates from the Berry curvature of gravitons, which has opposite signs for right- and left-handed helicities, and leads to a helicity-dependent splitting of the graviton energy Hall current. The magnitude of this splitting is found to be exactly twice that of the corresponding spin Hall current for photons.

Quantum computing is no longer a lab curiosity for academic research. Industrial processors exceeding 100 qubits are commercially accessible and, for the first time, can extract information from data in ways that classical algorithms struggle to match. The most direct way to monetize this capability for industrial production today is quantum feature extraction: turning raw business data (images, customer records, molecules, or sensor readings) into richer representations that outperform standard machine learning models. There is one obstacle, however, that stands between today's demonstrations and tomorrow's production systems: every sample of data costs a quantum computing execution. For a company with millions of customers, satellite images, or transactions per month, processing every sample on quantum hardware is simply not viable. This work introduces quantum feature surrogates, a framework developed by Kipu Quantum that breaks this bottleneck. The idea is intuitive though challenging: instead of asking the quantum computer to look at every single sample, we let it look at a small, carefully chosen subsample of the data, whose distribution faithfully represents the full set. A simple classical model, a surrogate, then learns the quantum-induced patterns and applies them to the rest of the dataset at near-zero cost. The quantum processor stops being a per-sample engine and becomes a teacher of representations, while production inference runs entirely on classical hardware.

Unifying the early-universe inflationary paradigm with late-time cosmic acceleration, while resolving the initial Big Bang singularity, remains one of the most profound challenges in modern cosmology. In this paper, we propose a non-singular quintessential inflation model embedded within the effective dynamics of Loop Quantum Cosmology (LQC) based on a Generalized Regularization Scheme. The quantum geometry effects naturally replace the initial singularity with a quantum bounce, followed by a phase of superinflation that sets robust initial conditions for the subsequent slow-roll inflation. To achieve a viable late-time dark energy epoch and address the coincidence problem, we introduce a coupling between the scalar field and massive neutrinos, known as Mass-Varying Neutrinos (MaVaNs). As neutrinos become non-relativistic in the post-inflationary evolution, their backreaction effectively freezes the scalar field, triggering the late-time accelerated expansion. We numerically trace the full background dynamics from the quantum bounce to the present day. Furthermore, we tightly constrain the model parameters utilizing the observational data, including the Type Ia supernovae sample, the Dark Energy Spectroscopic Instrument (DESI) Baryon Acoustic Oscillations (BAO) and Cosmic Microwave Background (CMB) distance priors. Our results demonstrate that this unified LQC-MaVaNs quintessential framework is highly consistent with current precision cosmological observations.

A fully quantum multiphonon up-pumping model is proposed to characterize coherent energy transfer in energetic materials (EMs) subjected to external shock. After eliminating the degrees of freedom of the phonon bath within a mean-field approximation, we derive a quantum master equation governing the energy transfer among vibrational modes. Our analysis reveals that doorway modes of different frequencies undergo distinct levels of effective coherent driving and dissipation, induced by the shocked phonon environment. This not only clarifies the microscopic origin of coherent phonon generation, but also reveals the possibility of modulating such coherent driving and dissipation. Based on numerical simulations of a simplified model using the master equation, we demonstrate how doorway modes extract energy from the phonon environment and subsequently excite higher-frequency molecular vibrational modes. This work offers a renewed perspective for understanding the mechanisms of energy transfer in energetic materials.

(1) Background: for the study of highly magnetized neutron stars observed as magnetars, and to quantify the effect of this intense magnetic field onto the star's structure and shape which can be particularly relevant for the study of emission of continuous gravitational waves, both numerical and perturbative approaches have been developed. (2) Methods: we compare these two approaches in General Relativity with the limitation to the case where the magnetic field has a purely poloidal structure. The perturbative one (Konno-99) assumes that the deformation induced by the magnetic field is small and that this field arises only from dipole currents. The full numerical one is based on the library LORENE. (3) Results: we have used both approaches to compute the magnetic field distribution and the deformation of the star, varying the value of the magnetic field at the pole, the compactness of the star and its equation of state. (4) Conclusions: whereas the perturbative approach breaks down for very high polar magnetic field values (typically above a few times $10^{16}$ G), it gives very good results for observed values, even in magnetars. On the contrary, the numerical code exhibits resolution problems for relatively low magnetic field values (typically $10^{10}$ G), which translates into imprecise computation of the star's deformation and mass quadrupole moment.

In this work, we investigate the non-Markovian features of the Adapted Caldeira-Leggett model, a computationally efficient framework recently proposed to capture the essential physics of the standard Caldeira-Leggett model. While this effective model has been previously validated for decoherence and einselection, its ability to reproduce memory effects remains to be explored. By exploiting the model's capability to explicitly track both system and environment degrees of freedom, we provide a detailed characterization of non-Markovianity through the lens of information backflow. We evaluate the buildup of system-environment correlations and the corresponding modifications of the environmental state, assessing a quantitative upper bound for the revival of distinguishability in the reduced dynamics. Our results, obtained by comparing different distinguishability quantifiers such as trace distance and the square root of the Jensen-Shannon divergence, show that while correlations are primarily sensitive to coupling strength, environmental state changes are more heavily influenced by temperature. Our analysis substantiates the physical interpretation of the distinguishability-based approach to non-Markovianity, and confirms this variant of the Caldeira-Leggett model as a reliable tool for exploring the microscopic origins of different fundamental phenomena in quantum mechanics.

We derive explicit formulas for the capacity of multimode quantum Gaussian channels which serve as a fundamental model for optical version of multiple-input multiple-output channels. We show that it is always optimal to increase the number of modes under fixed power constraint. We derive an analytical formula for the ensemble-averaged Holevo capacity in the case of random passive transformations. The analogous results are also obtained for capacities achievable under homodyne and heterodyne detection. We further discuss the generalization of the model to include weak active transformations.

Optically trapped polar molecules are promising for quantum information processing, yet the accuracy of an entangling molecular gate is limited by the uncertainty of dipole-dipole interactions~(DDI) from the molecular motion in traps. We show that two $π$ pulses of global microwave excitation can yield a high-fidelity controlled-phase gate when assisted by two single-qubit gates. The gate is resilient to the uncertainty of DDI because it does not rely on populating DDI-coupled states. Further, the controlled phase is fully tunable by varying the relative phase of the two global microwave pulses, and, hence, the gate can find applications in a wide range of quantum algorithms involving quantum Fourier transform. Moreover, we introduce a motional-mode separation technique to quantum mechanically study the influence of the molecular motion, which shows that the gate fidelity can be over 0.9999 with typical experimental conditions.

We show that Hilbert-space holonomy provides a geometric organizing principle for spectral reality in fragmented non-Hermitian many-body systems, complementary to conventional symmetry protection. In two minimal fragmented models, complex spectra can arise only within the most symmetric sectors: half filling in the fermion model and zero magnetization in the spin chain. Adding or removing a single particle, or flipping a single spin, renders the spectra entirely real despite unchanged periodic boundary conditions, reminiscent of boundary-condition sensitivity in systems with a non-Hermitian skin effect. We explain this by viewing nonreciprocal hopping amplitudes as a discrete gauge field on the Krylov graph: trivial holonomy permits a diagonal similarity transformation to the Hermitian limit, whereas nontrivial holonomy obstructs it and allows complex spectra. In certain regimes, trivial holonomy admits an emergent-boundary interpretation, and longer-range models exhibit finite real and complex regions governed by the same criterion.

Quantum programs are often shared as OpenQASM 3 circuits, but tests are still written in host languages such as Python with Qiskit. We present QUTest, a native framework in which both programs and tests are standard .qasm files. Tests follow the Arrange / Act / Assert pattern, while configuration, runtime requirements, and assertions are encoded as pragma comments (//%), preserving compatibility with existing OpenQASM tools. QUTest provides 12 assertion types spanning deterministic, statistical, quantum-state, and structural checks, plus a linter and an environment-aware mode for running the same test across selected runtime versions in isolated environments. Its CLI supports automatic test discovery, runtime compatibility checks, and XML reports for continuous integration. We describe the pragma language, implementation, and a planned evaluation using coverage and mutation testing. QUTest is available at https://github.com/QBugs/qutest. Video demo: https://youtu.be/FvgvsiAXuW0.

Within general relativity, black holes are widely regarded as the ultimate benchmark for compactness in the Universe. Recently, however, neutron star models have been constructed in a higher-curvature theory -- quasi-topological gravity (QTG) -- whose compactness can exceed the black-hole limit~\cite{LD19666}. Here we present a detailed analysis of both the equilibrium structure and radial stability of such configurations in QTG. By examining several representative equations of state and different values of the gravitational coupling constant, we find that in the high-central-density regime the compactness exceeding the black-hole bound exhibits a universal behavior in QTG. We further show that QTG corrections grow increasingly significant at large central densities and can stabilize configurations that are radially unstable in general relativity over a broad parameter range. These results establish ultra-compact neutron stars in QTG as theoretically viable strong-field configurations and provide a foundation for further investigations of their dynamical and phenomenological implications.

We study the capacity of entanglement in the microcanonical ensemble for an effectively additive bipartite system. Using typicality and the block structure of the microcanonical reduced state, we show that in the thermodynamic regime the capacity is controlled by energy-sharing fluctuations and can be expressed purely in terms of standard thermal response data of the subsystems. As an illustration, we apply the result to a toy model consisting of a Schwarzian ``black-hole'' sector coupled to a two-dimensional CFT radiation sector. At fixed total energy, the growth of the radiation sector forces the common temperature to decrease, producing a smooth Page-like single-hump curve for the capacity. The construction is meant as a thermodynamic microcanonical mechanism for Page-like capacity curves, rather than as a complete dynamical evaporation calculation.

For electromagnetism in Minkowski spacetime, the Bessel-Hagen method gives a particularly direct Noetherian derivation of the standard gauge-invariant energy-momentum tensor. The key step is to supplement the form variation generated by an infinitesimal coordinate transformation with a compensating electromagnetic gauge transformation. In this paper we ask whether the same idea can be applied to the massless spin-2 field described by the Fierz-Pauli action. We first prove that no nonzero local tensor quadratic in first derivatives of the symmetric field $h_{μν}$ can be strictly invariant under the spin-2 gauge transformation $h_{μν}\mapsto h_{μν}+\partial_μξ_ν+\partial_νξ_μ$; the direct electromagnetic analogue of the Bessel-Hagen construction therefore cannot exist. Once the inexact nature of the Fierz-Pauli gauge symmetry is treated correctly, however, the Bessel-Hagen construction does produce a gauge-invariant equivalence class of Noether currents. Changing the compensating spin-2 gauge parameter changes the current only by terms proportional to the Fierz-Pauli field equations; performing an independent spin-2 gauge transformation on $h_{μν}$ changes the current only by a trivial current given by the divergence of an antisymmetric superpotential plus field-equation terms. This provides the natural spin-2 analogue of Bessel-Hagen's electromagnetic construction, but only in the quotient space of conserved currents, and not as a preferred local gauge-invariant energy-momentum tensor.

Standard quantum mechanics is an idealisation based on infinite-precision objects: point states, exact probabilities, and sharp measurements. Yet every real experiment has finite resolution, and for macroscopic systems we never have access to the microscopic state. Following Heisenberg's call for a theory built only on observable quantities, and von Neumann's insight that a complete description of a macroscopic system is neither possible nor necessary, we elevate the macroscopic state to a fundamental concept. We introduce Interval Quantum Mechanics (IQM), in which the state of a quantum system is never a point but a quantum parcel - a basic weak-star open set of density matrices defined by finitely many open expectation intervals. Such a parcel is the exact mathematical representation of the set of all microscopic states that are compatible with the measured values of a finite set of macroscopic observables. We show that unitary evolution lifts to a deterministic flow on parcels, and that a finite-precision (fuzzy) measurement process is represented by a volume-contracting update that refines the initial parcel into a more constrained open set, strictly increasing the geometric information defined as the Hilbert-Schmidt volume of the parcel. By introducing a second impossible set, we obtain a double-parcel whose information increases monotonically - resolving the von Neumann entropy paradox. The framework eliminates foundational puzzles without additional interpretational assumptions: wave-particle duality becomes a smooth trade-off; Schroedinger's cat is never in a literal superposition; and the spooky action at a distance of entanglement disappears, replaced by a purely epistemic geometric update. All empirical predictions of standard quantum mechanics are recovered exactly in the infinite-precision limit, which is never physically attained.

Realizing a global quantum internet relies on the deployment of robust satellite-based entanglement distribution links. While pioneering demonstrations have established the feasibility of such links, the transition to operational infrastructure demands the validation of robust, integrated space-to-ground architectures. Here, we report on a free-space Quantum Key Distribution experiment conducted over a 1.8 km free-space link using an engineering model of the quantum payload onboard the SpeQtre satellite and the Abu Dhabi Quantum Optical Ground Station. By implementing a BBM92 protocol with polarization-entangled photons, a secret key rate of approximately 7.56 kbps with a mean quantum bit error rate of 4.78%+-0.24% was produced. The deployed system featured spectral and spatial filtering approaches identical to those in the space segment, thus validating the link budget and background rejection capabilities under realistic atmospheric conditions. These results confirm the operational compatibility between the ground and space segments, establishing a critical performance baseline for the SpeQtre mission and future space-based, large-scale quantum networks.

This paper reports a study on the formation and physical characteristics of compacts stars in AdS spacetime within the framework of Bose-Einstein Condensate. Considering a Bose-Einstein condensate background at zero temperature this study works on total mass, compactness, surface redshift, density, pressure, adiabatic index and energy conditions. The bosonic mass has been taken as three distinct functions of radial coordinate in exponential form, quadratic form, and power law form. Our results reveal that the mass increases monotonically with radius and remains within observational limit for all the observationally motivated compact-star mass scales considered in this study and the compactness for all the cases is within Buchdahl's limit and hence it was confirmed that the configuration correspondence to compact stellar configuration models rather than forming a collapsing model. Both NEC and SEC are satisfied throughout the stellar interior and hence dynamical stability is ensured. Furthermore, the study also confirms the enhanced mass concentration near the outer region in the stellar models under consideration. Hence present study explores the physical properties and stability of compact bosonic configurations in AdS spacetime within a holographically motivated framework. The present analysis is primarily phenomenological and qualitative in nature. The models considered here are intended to explore possible behaviours of self-gravitating bosonic configurations in AdS geometry and are not proposed as fully realistic neutron-star models.

We investigate the finite-time instability of massive spinning test particles around a rotating hairy black hole generated through gravitational decoupling. The particle motion is described by the full Mathisson-Papapetrou-Dixon equations with the Tulczyjew spin supplementary condition, and the sensitivity to initial conditions is measured using a ZAMO-projected finite-time Lyapunov analysis. The hairy deformation is controlled by two parameters: $α$, which sets the deviation from Kerr, and $β$, which changes the radial localization of the deformation. We show that spin-curvature coupling and the hairy geometry can shift the evolved orbit away from the requested seed parameters, making the empirical orbital map essential for interpreting the dynamics. Small-spin and geodesic trajectories remain close to regular behavior, whereas large-spin trajectories show stronger finite-time growth. A scan of the $(S,β)$ plane shows that the instability does not grow monotonically, but appears in localized regions where the particle spin and the radial profile of the hair act cooperatively. Thus, the hairy background does not simply rescale the Kerr result; it reorganizes the strong-field phase-space region sampled by spinning particles.

Quantum metrology aims to enhance measurement precision beyond the classical limit by leveraging quantum resources. Unlike multi-parameter dynamic quantum metrology, many questions regarding multiparameter quantum metrology at thermal equilibrium remain elusive. In particular, the ultimate precision limits achievable in this equilibrium setting are not yet well understood. In this work, we examine the fundamental limits of estimating multiple parameters with a quantum probe at thermal equilibrium. We first show that the Heisenberg limit with respect to the number of probes can be achieved, and our bound coincides with the known single-parameter bound when only one parameter is estimated. We then consider the low temperature limit, revealing a qualitatively different behavior compared to the finite temperature case. We give an example to illustrate the usage of our main results. Finally, we show the conditions under which the sensitivity bound can be attained and the optimal measurements to achieve it.

Intermediate-mass black holes (IMBHs) are the missing link in the cosmic hierarchy of black holes, bridging the gap between stellar-mass black holes and supermassive ones. They also serve as unique laboratories for testing strong-field gravity and are prime targets for future multi-messenger observations. However, IMBHs are a population that has remained notoriously difficult to detect. The microlensing effect of fast radio bursts (FRBs) can serve as a clean and powerful method to probe IMBHs. In this work, we develop a pipeline to search for microlensed FRBs based on their dynamic spectra and apply it to the CHIME/FRB Catalog 2. Two microlensing signatures have been identified in two separate sources, i.e. FRB~20190131D and FRB~20211115A. The inferred lens masses for these two signatures are $\sim[539-609]~M_{\odot}$ and $\sim[1544-2571]~M_{\odot}$, respectively. Here we interpret them as evidence for IMBHs. If there are no intervening structures-such as galaxies or clusters-along the line of sights for these two sources, the two identified IMBHs might be isolated and of primordial origins. In that case, we obtain primordial black holes (PBHs) within these two mass ranges would constitute $\sim4\%$ of dark matter. Moreover, if these two candidates are not genuine lensing signatures, the abundance of intermediate-mass PBHs with masses $>300,M_{\odot}$ is constrained to be $\sim13\%$ at $95\%$ confidence level. Therefore, more comprehensive observational information for FRBs, together with a deeper understanding of whether the intrinsic emission mechanisms of FRBs can produce lensing-like signals, will be crucial for establishing this effect as a powerful tool for probing (primordial) IMBHs.

We establish rapid mixing for Davies Markov semigroups associated with 2D Abelian quantum double models at any positive temperature. A condition of Dobrushin-Shlosman (DS) type holds at any temperature, and we show that the latter implies a modified logarithmic Sobolev inequality for the Davies Lindbladian. A key step in the argument is to verify a strong martingale condition for the local conditional expectations of the Davies semigroup in the regime of validity of the DS condition.

Absence of local conserved quantities, or \textit{nonintegrability}, is often assumed when discussing various phenomena in quantum many-body systems, such as thermalization and transport. However, no concrete proof of this property is known in electron--phonon coupled systems, a typical setting for condensed matter physics. In this paper, we show that the one-dimensional Holstein model has no nontrivial local conserved quantities other than the Hamiltonian itself and the total fermion number operator. We further show that the absence of nontrivial local conserved quantities also holds for the more general Holstein--Hubbard model. Our result has accomplished an advance in nonintegrability proofs by expanding their scope to systems in which particles with different statistical properties are mixed.

Nowadays, quantum communications provide a vast field of research in rapid expansion, with a huge potential impact on the future developments of quantum technologies. In particular, continuous variable systems, employing coherent-state encoding and quadrature measurements, represent a suitable platform, due to their compatibility with both the modulation and detection systems currently employed in standard fiber-optical communications. In this work, we address some relevant aspects of the field, and provide innovative results being also experimentally oriented. In particular, we focus on two relevant paradigms: quantum decision theory and continuous variable quantum key distribution (CVQKD). In the former case, we address the problem of coherent-state discrimination and design new hybrid receivers for binary phase-shift keying discrimination, obtaining a quantum advantage over conventional detection schemes, being also robust against typical experimental imperfections. In the latter scenario, we proceed in two different directions. On the one hand, we design new CVQKD protocols employing discrete modulation of coherent states, being a feasible solution compatible with the state of the art in optical communications technologies. On the other hand, we address the more fundamental problem of performing channel losses mitigation to enhance existing protocols, and investigate the role of optical amplifiers for the task. Finally, we make a first step towards a fully non-Gaussian CVQKD scheme by proposing, for the first time, the adoption of an optimized state-discrimination receiver, commonly adopted for quantum decision theory, within the context of CVQKD, obtaining a genuine quantum enhancement over conventional protocols.

We investigate a photolithography-only fabrication process for transmon Josephson junctions using a modified double-oblique evaporation geometry. Using a bilayer resist process and Al shadow evaporation, we fabricate junction structures and confirm by optical and scanning electron microscopy that the resulting narrowed crossing region reaches a geometrical area on the order of $10^4~\mathrm{nm}^2$, which lies in the size range relevant to qubit junction fabrication. Room-temperature resistance screening shows that the junction resistance falls within the target range for the present transmon design over a usable process window and exhibits a clear design dependence. We further implement fabricated junctions in transmon devices and evaluate them in a three-dimensional Al cavity at $20 \, \mathrm{mK}$, where we observe basic transmon qubit operation with $f_{01}$=4.865 GHz, $T_1 \sim 9 \, μ\mathrm{s}$, and $T_2^* \sim 0.4 \, μ\mathrm{s}$. These results demonstrate the feasibility of realizing functional transmon devices in a photolithography-only process using double-oblique evaporation.

Recently, it was discussed how the presence of a Killing horizon induces decoherence on a quantum system in a superposition of states. Focusing on the case of an electrically-charged system with superposed positions, this would happen due to ``entangling'' photons crossing the horizon while carrying information on the superposition. Purpose of this essay is to investigate this process in connection with black hole thermodynamics and the ensuing entropy bounds. We show that an apparent tension arising with the latter is resolved provided the entangling photons, expressing a modification of the field at, as well as inside the horizon, do not give rise to a flux across it. The storage of information in this field, not retrievable from an outside observer, causes the superposition to decohere.

We show that chain-like transients in head-on collisions of radially excited boson stars are controlled by the binary collision time, not by radial excitation alone. For selected \(n=2\), \(λ=400\) self-interacting configurations, isolated evolutions define breathing windows that serve as reference clocks. Numerical-relativity simulations show that visible chains form only when the collision time is compatible with the isolated breathing clock. A separation scan shifts the collision time relative to the same clock, confirming the timing-window mechanism.

This paper investigates the appearance characteristics of static black holes embedded in Dehnen-(1,4,5/2)-type dark matter halos with a quintessential field, focusing on how the dark matter halo and dark energy affect the black hole images. We first derive the event horizon radius and the photon effective potential of the black hole, and then calculate critical quantities such as the critical photon sphere radius and critical impact parameter under different parameter sets. Trajectories of photons are subsequently plotted. The study reveals that as the parameters of the dark matter halo (the central density of the dark matter halo $ρ_s$ and the scale radius of the central halo $r_s$) and the quintessential field (the normalization factor $c$ and the equation of state parameter of dark energy $w_q$) increase, the aforementioned physical quantities generally exhibit an increasing trend. Based on the derived general expressions for the redshift factor and integrated intensity, we further explore the optical effects of the spherical accretion and the thin-disk accretion models. The results indicate that dark energy exerts an influence on the black hole shadow that is strongly dependent on the observer's position, whereas the influence exerted by dark matter exhibits no such conspicuous dependence. Furthermore, dark matter and dark energy have distinct effects on both the intensity and the radius of the black hole shadow. In particular, the intensity exhibits a greater sensitivity to dark energy, whereas the radius is more responsive to dark matter. This distinction offers a potential observational criterion for identifying, through black hole images, whether the dominant interacting component near the black hole is dark matter or dark energy, and provides an important basis for constraining the equation-of-state parameter $w_q$.

We propose a theoretical scheme for quantum enhanced distributed network sensing, targeting multiphase estimation by leveraging multiple quantum resources. Specifically, we investigate the performance advantage in a distributed quantum network (DQN) for multiphase sensing by integrating three types of quantum resources(TQRs): quantum catalysis, entanglement, and squeezing. Our results reveal that employing all three TQRs leads to better sensing performance than using only two TQRs under both lossless and lossy conditions, with precision approaching the Heisenberg limit. We further demonstrate that partial quantum catalysis providesa stronger precision advantage than global catalysis in both ideal and noisy regimes. We identify a practical homodyne measurement scheme for globally and partially catalyzed multimode W type coherent states, whose measurement sensitivity can approach the corresponding quantum Cramer Rao bound. In this practical setting, partial catalysis also yields better measurement sensitivity than global catalysis. Moreover, under photon loss, both global and partial catalysis of multimode W type coherent states exhibit a loss catalysis dual enhanced sensitivity region. These findings highlight the quantum-enhanced advantages conferred by hybrid quantum resources for practical DQN sensing applications. Our work opens a way for realizing quantum-enhanced DQN sensing.

Multiqubit positive-partial-transpose (PPT) entangled states play an important role in quantum information theory. We characterize such states of minimum rank in three-qubit system, namely rank four. Depending on whether the Lorentz invariant is zero, we classify such states into two types. The PPT entangled states constructed by unextendible product bases (UPB) have nonzero invariants, which belong to type I. We provide a method to effectively determine whether a state can be constructed from UPB. For states with zero invariant, which belong to type II, we provide an explicit expression up to equivalence of stochastic local operations and classical communications (SLOCC). It turns out that we can represent them with only one complex parameter. We further study SLOCC-equivalence relation within the expression. We also investigate the Lorentz invariants of multiqubit states with rank less than three and analyze their range.

We propose a general architecture for universal logic operations using NAND and NOR gates on classical information encoded in period-doubled states of periodically-driven systems. The protocol involves applying a single pulse that simultaneously couple two input nodes with an output node. We show that the states of the nodes can be precisely controlled by tuning the coupling strength and pulse duration, allowing for robust logic gate operation. To highlight the universality of the protocol, we demonstrate its applicability on different systems, such as classical networks of dissi- pative parametric oscillators (DPO), quantum networks of Kerr parametric oscillators (KPO), and the periodically-driven open Dicke lattice model (DLM) emulating discrete time crystals (DTCs). We identify the parameter regimes in which the logic gate architecture is valid, and we showcase its robustness in the presence of fluctuations.

The cosmic dipole observed in the cosmic microwave background (CMB) is traditionally interpreted as being caused by the observer's motion relative to the background. However, tensions with dipole measurements from radio galaxy counts motivate the need for independent probes. This work investigates the feasibility of using strongly lensed gravitational wave (GW) events to measure the cosmic dipole. Strongly lensed GWs produce multiple time-delayed images, which can be used to infer the distances to both the lens and the source. These distances, associated with the observed redshifts of the lens and the source from galaxy catalogues, encode information about the background cosmology and cosmic dipole effects. By reconstructing a statistical sample of doubly lensed GW events based on the singular isothermal sphere lens model, the cosmic dipole can be estimated jointly with background cosmological parameters. Using realistic simulations for Einstein Telescope and Cosmic Explorer, we forecast that a dipole magnitude $g$ consistent with both the CMB and number count measurement could be detected with 10 years of observation. Furthermore, constraints on $g$ are greatly improved by combining constraints from doubly lensed events with those from triply or quadruply lensed events. In the most optimistic scenario, where we measure the number count dipole magnitude with 10 years of observation, we obtain $g = (2.45^{+1.53}_{-1.28}) \times 10^{-3}$ from the combined constraint, provided that systematic uncertainties are mitigated. Although challenging, strongly lensed GWs offer a novel approach to measuring the cosmic dipole, providing an independent consistency test with different systematics from electromagnetic probes.

One of the major challenges in realizing fault-tolerant quantum computers (FTQCs) is the requirement for a large number of physical qubits. To address this issue, high-rate quantum error correcting codes, which efficiently embed logical qubits into physical qubits, have recently attracted considerable attention. Among such codes, quantum BCH codes, which offer both high rates and large code distances, are promising yet underexplored candidates. However, no fault-tolerant ancilla preparation method specialized for this class had been established. We employ a two-stage approach (non-fault-tolerant preparation + entanglement distillation) for ancilla preparation. We then propose a framework for designing low-overhead distillation method that strategically leverages the cyclic symmetry of quantum BCH codes to determine which non-fault-tolerant circuits can successfully produce a fault-tolerant state. Numerical simulations on several high-performance quantum BCH codes up to 127 qubits demonstrate that our method achieves lower spatial overhead and logical error rates than conventional distillation circuits. Furthermore, we evaluated the logical error rates under a circuit-level noise model, and obtained performance benchmarks in realistic settings. This efficient state preparation technique is expected to contribute to the early realization of practical FTQCs, particularly on highly connected quantum platforms such as neutral atom systems.

We report precision spectroscopy of ionic-core transitions in alkaline-earth Rydberg atoms. We demonstrate high-resolution measurements of isotope shifts and hyperfine splitting of dipole transitions in ionic cores which have not been explored so far. A key element of this work is the reduction of the linewidth by more than two orders of magnitude enabled by dynamical control of Rydberg electron's orbit which significantly enhances the spectral resolution. Furthermore, to unambiguously identify the frequency shift, we directly compare core ion's spectrum with a signal from a single trapped ion serving as an ultimate frequency reference. This work provides an important foundation for quantum control of inner-core transitions, which offer an useful tool in manipulating Rydberg atom as well as a sensitive probe for electron-core interactions in atomic and molecular systems.

We study when local reduced density operators, viewed as quantum marginals, can be assembled into a global quantum state with a prescribed Markov structure. The starting point is a canonical logarithmic construction $T(\mathcal R)$, the noncommutative analogue of the junction-tree formula for decomposable graphical models. Unlike in the classical case, this formal construction may fail: noncommutativity can prevent it from being a normalized state with the prescribed marginals. We prove that this obstruction is captured exactly by a trace condition. For two overlapping marginals, and for clique marginals on a chordal graph, the condition $Tr(T(\mathcal R))=1$ is equivalent to the existence of a quantum Markov completion. When it exists, the completion is unique, equal to $T(\mathcal R)$, and selected by the maximum-entropy principle. In the two-clique case, we also give an equivalent conditional-reconstruction characterization: the two natural one-sided sandwich reconstructions agree if and only if the trace condition holds. We introduce the global quantum information $gI(\mathcal{G})_ρ$ associated with a chordal graph $\mathcal G$ and show that it is a relative-entropy discrepancy from $ρ$ to the logarithmic candidate, with a trace correction when the candidate is not normalized. We also prove an intersection property for strictly positive quantum conditional independence. Three-qubit Pauli examples show that the quantum obstructions are real: local consistency, feasibility, Markov feasibility, and maximum entropy can all separate.

Single-photon emitters in nanophotonic structures are a key building block for many photonic devices with quantum technology applications, like quantum sensors and quantum computers. In this paper, we determine the exact dynamics of a single-photon emitter in a one-dimensional waveguide terminated by a partially-transparent mirror interface, by solving the Schrodinger equation via a local-photon approach. In general, the evolution of the emitter is non-Markovian, characterized by a non-exponential decay profile. The decay can resemble an exponential after a time that is much larger than the emitter-mirror round-trip time and becomes exponential in the Markovian limit, where the round-trip time between the emitter and the mirror is neglected. We also derive the spatial and spectral profile of the emitted photon wave packet and demonstrate how its properties are altered by the environment.

We study the charging performance of a one-dimensional, many-body bosonic quantum battery driven by a harmonic-oscillator charger, focusing on how many-body effects and intra-species interactions influence the energy-transfer dynamics. We show that by tuning the charger frequency, the system can reach a resonance condition where perfect energy transfer and maximal extractable work are achieved. In the weak-coupling limit this can be understood by approximating the battery-charger dynamics using an effective two-level model, which accurately predicts the maximum stored work, ergotropy, and optimal charging time. In this regime, many-body batteries exhibit enhanced charging power, reduced quantum speed limit (QSL) times, and comparable or lower irreversible work relative to single-particle batteries. We further examine the role of intra-species interactions: repulsive interactions inside the battery medium suppress performance, whereas attractive interactions can significantly enhance it, with both types of interactions generating additional charging resonances. Our results show that particle number and interaction control provide powerful tools for designing fast, efficient, and scalable quantum batteries, and point toward a feasible experimental implementation in ultracold-atom platforms.

Quantum Transfer Learning (QTL) offers a promising approach for visual quantum machine learning under near-term constraints, where limited qubit counts, shallow circuit depths, and costly hybrid optimization restrict end-to-end quantum training. In this setting, pretrained classical backbones can extract high-level visual features, while compact quantum modules operate as trainable classification heads. However, existing QTL results are difficult to compare because they often differ in datasets, preprocessing, backbone settings, qubit budgets, circuit designs, optimization choices, and reporting protocols. This work presents a controlled benchmarking methodology for evaluating representative QTL methods under a unified transfer-learning pipeline. The benchmark compares DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, and ED-QTL under shared preprocessing rules, frozen-backbone settings, training conditions, and reporting metrics. The evaluation focuses on Fashion-MNIST and Hymenoptera Ants vs Bees as the two main datasets, while CIFAR-10 is used to provide additional configuration-level evidence on a harder natural-image task. Beyond predictive performance, the benchmark analyzes circuit size, trainable parameters, quantum parameters, training time, and architectural sensitivity to qubit count and circuit depth. The results show that no single QTL family dominates across all settings: performance depends on the dataset, encoding strategy, circuit design, and computational cost. These findings highlight the need for resource-aware QTL evaluation and provide guidance for selecting hybrid quantum-classical transfer models under near-term resource constraints.

Programmable quantum annealers are used as open-system samplers, but it is unclear when reverse annealing erases preparation memory and what the readout represents. Here we implement a subsystem-environment protocol on two D-Wave quantum annealers, varying environment size, coupling, disorder, preparation, geometry and QPU generation. A six-qubit subsystem becomes initial-state independent when the environment is large or strongly coupled, while quenched disorder and atypical environment states arrest relaxation. Pairing the memory order parameter with the distance to a calibrated conditional-Boltzmann reference yields a diagnostic that flags rare wrong-basin trapping memory loss alone misses; memory-retaining conditions stay far from the reference (median 0.35). Relaxed ferromagnetic readouts are near-deterministic, so small distances there are a consistency check, not a thermometric test. In a mixed-frustration benchmark, the local-update model practitioners assume mispredicts QPU relaxation roughly sevenfold, whereas non-local classical sampling recovers it. We provide a subsystem-level validation protocol for quantum-annealer sampling.

Standard CMB analysis assumes a direct deterministic mapping between the multipole probed by the CMB $\ell$ and the primordial wavenumber $k$. Since the recombination era has a finite duration, this mapping is probabilistic by construction. We elevate the power spectrum of the primordial perturbations to a probability distribution caused by the finite duration of the recombination era. We show that a finite recombination width introduces a Gaussian smoothing scale in $\ln k$ with $σ_{\ln k} \sim σ_η/ D_*$, leading to a probabilistic mapping from multipoles to inflationary e-folds. This effect is zero in standard power-law inflationary scenarios, but it may become relevant for scenarios with exotic oscillating features of the primordial power spectrum, which will be probed by the future CMB experiments. The observed effective power spectrum is the true primordial spectrum blurred by the uncertainty in scale reconstruction, which is mathematically identical to a Bayesian marginalization over a latent variable, and thus there is a propagation of the measurement error in the independent variable, which is another more formal way to view the smoothing effect. Our results indicate that the smoothing has quantifiable effects on the spectral index and its running, but more importantly the difference between the TT and EE inferred spectral indices, $n_s^{TT}-n_s^{EE}$, is non-trivial, in contrast to standard inflation without smoothing, and might become observable by future cosmic microwave background experiments. Any tension in $n_s^{TT}-n_s^{EE}$ could indicate oscillations in the primordial spectrum and the effects of the power spectrum smoothing. Finally, a minimal Fisher matrix analysis is performed to investigate the observability prospects of the smoothing effect.

The gradient expansion and the separate universe approach provide an effective description of inflationary soft modes after coarse-graining shorter-wavelength degrees of freedom. We formulate a locality condition on the quantum state, requiring that the hard-mode state in each local universe depend on the soft modes only through the local soft-mode values in the same patch. When this condition is satisfied, the coarse-grained soft-mode dynamics remains local, and loop corrections from hard modes to superhorizon correlators of the adiabatic curvature perturbation are perturbatively suppressed. This provides a model-independent diagnosis of when enhanced corrections due to hard modes can invalidate the gradient expansion. We further show that the same locality condition implies a generalized soft theorem, from which the standard consistency relations follow under additional assumptions. This formulation clarifies the origin of possible deviations from the standard consistency relations in multi-field systems or in a non-attractor background. We also show that the locality condition guarantees the absence of infrared divergences for the correlators of operators invariant under a large gauge transformation. Thus, locality of the hard-mode state provides a unified criterion for the effective description of inflationary soft modes, generalized soft theorems, the suppression of hard-mode loop corrections, and the infrared regularity of observable correlators.

Quantum error-correcting codes with translation symmetry and local checks have been studied extensively, leading to a wide variety of fracton codes in three or more dimensions which lack a complete unifying picture. Recently, the study of translation-invariant codes with long-range checks has revealed impressive performance for small fixed-size instances in two dimensions. Here, we provide a unifying picture for a large family of translation-invariant codes, both local and long-range, that captures many fracton codes and all Abelian Two-Block Group Algebra (A2BGA) codes, including the Bivariate Bicycle (BB) codes. The balanced product structure of A2BGA codes leads to a local parent code that is a hypergraph product fracton model in a higher dimension. Different compactifications of a parent code produce a wide variety of descendant codes which provides a unifying picture for their properties. In particular, all BB codes with the same check weight are derived from a single parent hypergraph product fracton model. This construction allows us to extend Wang and Pryadko's code-parameter bounds for Generalized Bicycle codes to A2BGA codes. We conjecture that the transversal gates and energy barriers of the translation-invariant descendant codes are limited by those of their parent fracton models.

The PXP chain was recently shown to exhibit superdiffusive energy transport with Kardar-Parisi-Zhang-like scaling, $z\approx3/2$, joining a growing number of spin chains with this exponent. An understanding of how this anomalous hydrodynamics emerges from microscopics is, however, still lacking. In this work, we show that finite-temperature energy transport in this model provides a window into the emergence of superdiffusion. At finite temperature, the energy autocorrelation function exhibits a crossover from short-time coherent dynamics to long-time hydrodynamics. The short-time behavior is dominated by a single magnon band and can be understood analytically. In momentum space, this regime is characterized by spectral weight near $q=π$. The damping time $τ$, which separates the short-time magnon-dominated behavior from the late-time hydrodynamics, grows rapidly upon cooling, consistent with an activated form $τ(β)\sim βe^{Δβ}$ with a gap scale set by the magnon band. At longer times, the spectral weight transfers to $q=0$ and the running decay exponent drifts toward the superdiffusive value $z=3/2$. Finite-temperature energy transport therefore provides a bridge between microscopic magnon physics and late-time superdiffusion in the PXP model.

Following the success of Moore's predictions, we are approaching a limit in the miniaturization of semiconductors for computing materials. This has led to the exploration of various research paths to develop alternative computing paradigms, such as quantum computing, 3D transistors, molecular logic, and continuous logic. In this context, we propose a novel approach in which the dynamics of a two-level atom is used to execute classical Boolean logic operations. Unlike traditional combinational logic circuits, where the output depends solely on the input, we suggest a finite-state machine-like computing model, where the output is influenced by both the input and the system's initial state. The proposed mechanism leverages the dynamics of a two-level quantum state, with information encoded in observable quantities. These observables, the density matrix's population (diagonal) and coherence (off-diagonal) elements, were analyzed using the Liouville equation. The selection of observables within the Liouville space allows us to encode more variables. Although environmental noise may cause some loss of encoded information, fast computations can still be performed before it dissipates. In addition, logic operations can be read in parallel, enabling complex computations. This system can also be scaled to an N-level configuration.

We formulate quantum geometry for non-Hermitian systems under open boundary conditions. By defining quantum-geometric quantities in both real-space and non-Bloch representations, we establish a unified framework beyond conventional Bloch band theory. Our central result is an exact equivalence between the real-space integrated quantum metric and a non-Bloch integrated quantum metric defined on the generalized Brillouin zone. We further introduce localized non-Bloch Wannier functions in the presence of the non-Hermitian skin effect and show that the non-Bloch integrated quantum metric gives the gauge-invariant part of their spread functional. These results establish quantum geometry as a natural framework for characterizing open-boundary non-Hermitian band structures and the localization properties encoded in skin modes.

We consider $(n,k)$ MDS-coded distributed storage over $\mathbb{F}_q$ with per-node storage $α$ symbols. For the oblivious update problem, where a single message symbol changes and neither helpers nor the stale node know which, the classical lower bound is $αk \log_2 q$ bits. We prove that when the $k$ contacted helpers share prior quantum entanglement, the update bandwidth is $\lceil α/2 \rceil \cdot k \log_2 q$ bits-equivalent, a factor approaching 2 reduction. For $α= 2$, a $[[k, k-2]]_q$ CSS code achieves bandwidth $k \log_2 q$ with one qudit per helper. For general $α$, a $[[\lceil α/2 \rceil k, \lceil α/2 \rceil k - α]]_q$ CSS code achieves the bound with $\lceil α/2 \rceil$ qudits per helper. The matching converse uses the superdense coding bound: the stale node holds all transmitted qudits and hence the entangled partners, so each helper's channel supports at most $D^2$ distinguishable signals for dimension $D$. The result holds for all $(n,k)$ pairs with sufficiently large prime $q$.

Neutral-atom entangling gates are commonly analyzed with a single effective Rydberg-pair state, but near Förster resonances the pair manifold contains resonantly coupled interaction channels that change both the control landscape and the achievable fidelity. We develop a two-eigenstate model for this regime and show that when allowing for coupling to both pair states in the resonance, the gate fidelity is bounded by $\mathcal{F}\leq 1-(π/2)/(Vτ_R)$, for interaction strength $V$ and Rydberg lifetime $τ_R$. We construct a gate protocol that saturates this bound in the large-Rabi-frequency limit, improving the existing fidelity limit by approximately $40\%$. We also evaluate common gate protocols near Förster resonances and find that retaining the exchange dynamics increases predicted fidelities by up to two orders of magnitude over earlier treatments.

Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive anomaly detectors. We present a leakage-free evaluation of quantum machine learning for UAV anomaly detection on the multi-sensor TLM:UAV benchmark. Three contributions support the study. (i) A group-aware temporal protocol (B2) partitions the dataset into ten contiguous TimeUS blocks and evaluates over ten seeds, eliminating the inflation produced by random stratified splits that mix neighbouring samples. (ii) A three-mode feature audit (full/loose/strict) quantifies how much accuracy stems from instantaneous physical signals versus contextual proxies (cumulative energy, battery state, GPS trajectory). (iii) A hybrid XGBoost + Data Reuploading (DRU) classifier is benchmarked against five paired non-linear controls (raw, PCA, polynomial-2, random-RBF, and an untrained DRU map) under identical budgets. The standalone DRU does not consistently match the strongest classical baseline across seeds; however, the trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05), a directional signal that the per-seed standard deviations prevent from being interpreted as a statistically established difference. The trained-DRU hybrid also records the lowest mean false-alarm rate under proxy-free evaluation, subject to the inter-seed variance reported. We frame this as an incremental, reproducible quantum-enhanced hybrid benefit, and provide an open Qiskit 2.x implementation as a benchmark for cybersecurity analytics in NISQ-era aerospace systems.

We investigate thick-brane configurations in five-dimensional $f(T,T_G)$ modified teleparallel gravity. In five dimensions, the torsional Gauss-Bonnet invariant $T_G$ contributes dynamically, leading to genuinely new effects even at linear order. Within a warped geometry supported by a scalar field, we construct explicit solutions and show that the $T_G$ sector significantly modifies the brane structure. In particular, the coupling parameter controls the deformation of the warp factor and energy density, allowing for the emergence of brane splitting and nontrivial internal structure. We further analyze the localization of spin-$1/2$ fermions via a Yukawa coupling. The system admits a normalizable chiral zero mode, while the opposite chirality remains delocalized. The massive Kaluza-Klein spectrum is strongly affected by the torsional Gauss-Bonnet term, which modifies the effective potentials and leads to the appearance of resonant quasi-localized states.Our results show that $f(T,T_G)$ gravity provides a richer framework for braneworld models, where torsional higher-order corrections play a key role in shaping both geometry and field localization.

We develop a family of quantum accreditation protocols for quantum circuits with non-Clifford two-qubit gates. The latter includes families of gates such as the fSim and XY families of gates, native to existing hardwares. We provide practical and scalable protocols that upper-bound the total variation distance between the probability distributions of error-free and erroneous quantum computations. We also establish the robustness of our protocols to small perturbations and generalize Pauli twirling to non-Pauli single-qubit bases, which may be of independent interest.

Fisher-information inequalities have recently been used as operational witnesses of nonclassical metrological behavior, but their physical meaning is often tied to a particular narrative, such as, segmented dynamics or discrete trajectories. We show that a broader interpretation is available and, in fact, more natural: once an experiment is assumed to admit a classical causal model specified by a directed acyclic graph, conditional independences, and modular parameter dependence, the corresponding Fisher informations are forced to obey causal Fisher-information inequalities (CFIIs). The backbone result is a causal-path series law: for an additive causal parameter that propagates through a classical path $A \to C \to B$, the inverse Fisher information behaves as an information resistance and must add in series. Consequently, any CFII violation is a rigorous falsification of the entire classical causal model class. We then show that the violation is automatically a metrological resource certificate, because it implies a precision that no member of the classical causal class can attain. The gain mechanism is identified as Fisher-information synergy, i.e. off-diagonal score correlations that classical modularity forbids. A single-qubit coherent-rotation example demonstrates the deterministic CFII violation, estimator-level achievability of the resulting gain, robustness against split-optimized classical benchmarks, and a chain-amplified advantage in long causal decompositions. Finally, an AI-assisted adversarial finite-data stress test shows that the witness remains certifiable under the realistic visibility loss and readout error, while optimized modular classical causal models saturate but do not cross the CFII frontier.

The Koopman--von Neumann (KvN) formulation of spectrally truncated fluid and plasma dynamics is considered as a potential approach for quantum computation. The KvN framework embeds the Liouville equation into a Hilbert space with norm-preserving, unitary evolution. Here, we propose a Weyl-ordered KvN generator along with a summation-by-parts discretization, which ensures that the resulting operators are exactly unitary as required for quantum computers. The Weyl-ordered KvN generator is derived as the unique anti-Hermitian operator symmetrization for real velocity fields. The formulation operates directly in the physical amplitude space without phase-space doubling, so the Heisenberg uncertainty principle does not constrain the grid resolution during evolution. This limitation re-enters only at the measurement stage on a quantum computer. Exact discrete unitarity is proved as a purely algebraic identity that holds regardless of grid resolution or stencil order. To manage boundaries, a split-step Kraus absorbing layer is introduced via a Stinespring dilation requiring only one ancilla qubit. Validation on three test cases spanning dissipative and Hamiltonian regimes (a viscous Navier--Stokes triad, an incompressible Euler triad, and a Hasegawa--Mima drift-wave triad) confirms fourth-order convergence and machine-precision unitarity.

Lu \textit{et al.} [Ann. Phys. (Berlin) \textbf{534}, 2100550 (2022)] posed the question of whether bound entangled states can be used for ancilla-assisted quantum process tomography (AAQPT). In this work, we answer this question in the affirmative by explicitly demonstrating that certain bound entangled states can be used for AAQPT. We further show that, although local filtering operations may improve the trace norm of the realignment criterion, they are essentially ineffective for AAQPT, as they render the resulting states \emph{unfaithful} and therefore unsuitable for reliable process reconstruction. Further, we investigate the efficiency of bound entangled states for AAQPT and establish quantitative bounds by comparing their performance with Werner and isotropic states. Our results therefore provide a new application of bound entangled states in the context of ancilla-assisted quantum process tomography.

Recently, we showed that the possibility of an interstellar mission to the closest black hole, while highly speculative and extremely challenging, is not completely unrealistic within the next few decades. Since such a mission might last around a century and require significant financial and human resources, it is crucial to assess whether it can truly study black holes and test General Relativity at levels unattainable by observational facilities in the Solar System for many years. In this manuscript, we assume the capability to decelerate the spacecraft and present a preliminary study of how probes orbiting a black hole could test the nature of the compact object.

Gravitational-wave observations of merging binary neutron stars and black holes are now routinely made by detectors in the Advanced LIGO-Virgo-KAGRA network. Neutron star binary systems may also produce detectable electromagnetic and particle emission over times scales ranging from seconds to years. Real-time gravitational-wave searches play a central role in enabling time-critical electromagnetic and/or neutrino follow-up observations. During the fourth observing run (O4) of the Advanced LIGO-Virgo-KAGRA network, multiple real-time searches operated continuously to identify candidate gravitational-wave events and publicly disseminate information about these discoveries. Here, the performance and results of the GstLAL real-time analysis are reported. The analysis is designed to identify candidates with low latency, high detection efficiency, and sustained operational uptime over long observing periods. Across O4, it produced initial candidate uploads with a median latency of 15.8 s while maintaining an effective uptime of 98% during the first two parts of the observing run. During the run, the analysis contributed to 250 candidates classified as astrophysically plausible, provided the first upload for 222 of these, and was the sole contributor for 75. Among Gravitational-Wave Transient Catalog events with a false-alarm rate below one per year, 88% were identified as significant in low latency and promoted for expert vetting and public dissemination. The low-latency astrophysical classifications agreed with the final catalog classifications for 93% of the events considered.

We prove the weak cosmic censorship conjecture in $2+1$ spacetime dimensions for the circularly symmetric Einstein-scalar field system in the presence of a negative cosmological constant $Λ<0$. More precisely, we show that for any integer $k\geq2$, the maximal development of generic $C^k$ initial data does not contain naked singularities. An essential step of the proof is establishing the presence of a mass gap. In particular, this implies that all naked singularities have infinite blueshift, which represents the main instability mechanism.

We study the quantum dynamics of the rotational degree of freedom of a nano-magnet trapped in a superconducting trap. The nano-magnet is modeled as a magnetic dipole with magnetization pinned to the easy axis of the particle. The magnetic trap then leads to a potential barrier that hinders free rotation of the particle, but through which it can tunnel. We identified rest-gas scattering as the most important decoherence mechanism at low temperatures. A shape of the particle sufficiently close to perfect rotational symmetry about the rotational axis can protect the rotational tunneling against this decoherence mechanism, and we identify experimentally feasible parameter regimes where rotational tunneling should be observable.

In this work, we investigate the feasibility of using ringdown waveforms from supermassive black holes immersed in dark-matter halos to extract both the intrinsic black-hole parameters and those characterizing the surrounding matter distribution with future space-based gravitational-wave detectors. Building on the fully relativistic framework developed by Cardoso {\it et al.}, in which the dark-matter degrees of freedom are explicitly accounted for by minimal coupling to the gravitational sector, we construct numerical waveforms for a variety of Dehnen-type dark-matter profiles. We then convert these simulated waveforms into realistic data streams for future space-based gravitational-wave observatories, consistently implementing the second-generation Time-Delay Interferometry scheme in the analysis. We calculate the signal-to-noise ratios and perform a Bayesian parameter estimation to infer the model parameters, quantifying their measurability through the resulting posterior distributions. Our results indicate that the presence of dark matter can induce sizable modifications to the waveforms through the appearance of fluid modes at late times. Furthermore, dark-matter profiles with more pronounced spikes leave stronger imprints on the gravitational-wave signal, thereby enhancing the prospects for parameter inference with future space-based detectors such as LISA, Taiji, and TianQin.

All standard scalar quantum-information measures -- concurrence, negativity, entanglement entropy, the optimized CHSH bound, and quantum Fisher information -- are CP-blind in ideal \\ spin-0 $\to f\bar f$ decays because the two-qubit spin state is maximally entangled for every CP angle. We show that stabilizer magic, fixed in the physical Pauli frame of spin analysis, escapes this blind spot: the stabilizer Rényi entropy admits an exact closed form, vanishing at CP-definite and Clifford phases and peaking at maximal non-Clifford mixing. Two experimentally accessible, magic-inspired CP witnesses follow; the linear amplitude is $14.3\times$ more efficient than its quartic counterpart and reaches discovery-level sensitivity at the HL-LHC for $H\toτ^+τ^-$.

Scalable arrays of individual atoms provide an ideal starting point for quantum information and simulation experiments. However, their preparation is often limited by light-assisted collisions (LACs), which typically result in parity-projected filling fractions of $f \approx 0.5$. In this work we demonstrate a light-assisted redistribution process in the Quantum Matter Synthesizer that overcomes this constraint by stochastically moving atoms from multiply occupied lattice sites to neighboring vacant sites. Using a blue-detuned optical pumping beam during degenerate Raman sideband cooling, we achieve single-atom filling fractions of $70-80\%$. We find that over 50$\%$ of the atoms involved in radiative collisions are retained in the lattice. The redistribution process involves many LACs over an extended time as atoms diffuse to empty sites. Our demonstration offers a scalable and efficient pathway toward unity-filled atom arrays without the need for complex rearrangement protocols, with broad applicability to quantum simulation, precision measurements, and quantum information control.

Freezing is usually used to suppress unwanted dynamics, but it can also be used to engineer interactions. We introduce freezing-induced interaction engineering, a drive-only control paradigm in which dynamically freezing an auxiliary subsystem reshapes the effective Hamiltonian of the remaining degrees of freedom. As a concrete realization, we consider a three-qubit architecture where a driven modulator $M$ is coupled to one of two target qubits, $Q_1$, while $Q_1$ and $Q_2$ retain a fixed native exchange-type interaction. When $M$ is frozen in a dressed eigenstate, its projection renormalizes the local Hamiltonian of $Q_1$. This makes the dressed-frame detuning between $Q_1$ and $Q_2$ controllable by the drive frequency. The native interaction can then be switched between two regimes: an interaction-off regime with large dressed-frame detuning, and an interaction-on regime with resonant exchange. In the interaction-on regime, the protocol realizes an iSWAP gate using the native $Q_1Q_2$ coupling. Full lab-frame simulations show high-fidelity iSWAP dynamics and strong interaction suppression in the interaction-off regime. By combining native-coupling gate speed with drive-only operational simplicity, freezing-induced interaction engineering provides a route toward fast, drive-controlled entangling gates in fixed-frequency quantum architectures.

We develop an operator-algebraic framework for modular self-duality, symmetrized relative entropy, and Bogoliubov--Kubo--Mori susceptibility of local states in quantum field theory. In finite dimensions, modular self-duality singles out fixed points at which a state coincides with its modularly reflected partner. At such points, the natural comparison functional is the symmetrized Umegaki relative entropy. It vanishes at coincidence, and its Hessian is governed by the Bogoliubov--Kubo--Mori quantum Fisher information along the reflected tangent direction. We then extend this fixed-point construction to the local type~III von Neumann algebras that arise in quantum field theory. Here, a local state is compared with the modular pullback of its commutant restriction, and the intrinsic comparison functional is the symmetrized Araki relative entropy. For sufficiently regular state deformations, the fixed-localization Hessian at the self-dual point defines a type~III Bogoliubov--Kubo--Mori susceptibility. This coefficient is obtained by evaluating the BKM bilinear form on the tangent selected by the modular pairing. Exact coherent-state realizations are obtained for the free scalar field on wedge algebras and for the chiral \(U(1)\) current on half-line algebras. In both examples, the comparison functional is exactly quadratic in the deformation parameter, and the susceptibility coefficients admit explicit boost-energy, stress-tensor, or half-line integral representations.

We define an observable-space framework of Quantum Koopman Algorithms (QKAs) for simulating the dynamics of both linear quantum and nonlinear classical systems, based on approximately closed sets of observables and efficient coherent encodings of their Koopman-driven evolution. QKAs have two strands: Dynamic-QKA for the initial-value problem of observables dynamics, and Spectral-QKA for the eigenvalue analysis of the Koopman operator. We demonstrate the scope of the framework through several applications. First, for classes of $N$ free fermions linearly coupled to a bath, we construct quantum algorithms with gate cost $O(\mathrm{polylog}(N))$, an exponential improvement over classical methods, and use them to reconstruct heat flows and decay rates. Second, for nonlinear classical dynamics, we introduce a novel nonlinear interaction-picture quantum algorithm that enables perturbative expansions around solvable nonlinear reference flows, going beyond existing approaches that only apply to weakly nonlinear systems. Third, we develop spectral methods for extracting eigen-frequencies of late-time nonlinear dynamics, introducing a windowed quantum ODE-solver. Our results identify the Koopman-quantum interface as a natural setting in which quantum algorithms can exploit observable-space structure to simulate both classical and quantum dynamics.

We use Deutsch's algorithm as a stand in for more complex quantum algorithms in order to determine how quantum properties of an environment manifest themselves in results that can be obtained on quantum computers. We model pure dephasing in two different ways; one keeps the full density matrix of the qubits and environments (quantum) while the other uses Kraus operators (classical). We find that a single run of the algorithm yields the same effect in both cases, but running the algorithm twice leads to stark differences. Taking correlations and interplay between different decoherence processes into account leads to a slowing of decoherence effects for balanced functions. For constant functions, the effect is much more pronounced, and there is a qualitative change in the dependence of measurement outcomes on decoherence. We present results obtained on one of the IBM Quantum processors, which fully reproduce the predicted effect regardless of the assumptions made in the derivation. We further illustrate the findings on NV center spin qubits, which show more complex behavior due to a small size of the environment.

We show that a non-interacting gas of charge-$e/3$ anyons with exchange statistics $θ=-π/3$ can superconduct through a hidden weak-pairing mechanism. Such an anyon gas arises naturally in doped fractional Chern insulators at filling $1/3$ or $2/3$, where projective lattice translations enforce three degenerate anyon pockets. Exploiting this three-pocket structure, we develop a flux-attachment construction in which the average statistical flux vanishes, thereby mapping the problem to three species of composite fermions (CFs) in zero effective magnetic field. We show that the anyon statistics itself, encoded in statistical gauge field fluctuations, supplies the pairing glue and drives the CFs into a $p-\mathrm{i}p$ paired state, which corresponds to a $f-\mathrm{i}f$ physical superconductor. The CF strong-pairing phase is adiabatically connected to Laughlin's picture of anyon superconductivity, where charge-$e/3$ anyons bind into charge-$2e/3$ molecules, which then lead to superconductivity. By contrast, the more natural weak-pairing phase of CFs realizes a distinct superconducting phase - its edge is characterized by a chiral central charge $c_-=-1/2$, in contrast to the prediction of integer $c_-$ for the anyon superconductor based on Laughlin's picture, thereby resolving the discrepancy between previous theories and recent numerical results. Our theory provides a natural framework for understanding superconductivity near fractional Chern insulators, as observed in recent experiments. Finally, we discuss extensions of our theory that predict new chiral superconductors adjacent to FCIs at other fillings.

Gutzwiller's trace formula has a central place in quantum chaos because it provides semiclassical approximations for quantum energy levels in classically chaotic systems by linking them to classical periodic orbits. In this didactic article, we discuss a derivation of the trace formula starting from the Feynman path integral. We then describe how the trace formula is used to explain universal features in the distribution of the quantum energy levels that are described by random matrix theory, and we give an overview of related work.

We investigate nonequilibrium transport in a triple-quantum-dot (TQD) system, where the central dot acts as a discrete tunnel barrier, subject to continuous monitoring by a quantum point contact (QPC) that is capacitively coupled to all three dots with independently tunable strengths. We show that this global measurement scheme affects transport in a qualitatively distinct manner from single-site measurement. By engineering structured dephasing, measurement provides a significant improvement in the barrier occupation and tunneling current. In the strong-measurement limit, the steady state becomes independent of the underlying Hamiltonian parameters, and the barrier occupation can approach 1/2 for suitable measurement configurations. We identify an optimal measurement configuration that maximizes the steady-state current and show that near-optimal performance can be achieved with a simple central-dot readout scheme.

We study the fermionic non-Gaussianity in typical quantum states, focusing on Haar random states of qubits with or without a global $U(1)$ symmetry. Using the Weingarten calculus, we derive analytical predictions for the non-Gaussianity, defined as the relative entropy between the reduced density matrix and its Gaussianized counterpart. We identify two regimes controlled by the ratio between the subsystem and the system size, $\ell/L$. For $\ell/L < 1/2$, the non-Gaussianity vanishes in the absence of symmetries, because typical reduced density matrices are exponentially close to the maximally mixed state. In the presence of a global $U(1)$ symmetry, instead, it remains small but finite. By contrast, in the regime $\ell/L > 1/2$, the non-Gaussianity becomes extensive. These results establish the typical scaling of fermionic non-Gaussianity in random states and analyze how this is modified by the presence of global symmetries.

We investigate ancilla-free linear combination of unitaries (LCU) as a framework for approximating complex quantum circuits. This is particularly effective for quantum optimization algorithms, where candidate solutions can be evaluated classically and the task is to sample high-quality bitstrings rather than reproduce the full output distribution. We show that Fourier-based LCU constructions efficiently decompose broad classes of diagonal and non-diagonal unitaries, replacing highly connected qubit interactions with single-qubit gate layers or significantly simpler structures at the cost of a polynomial sampling overhead. Applied to algorithms such as QAOA, this yields efficient, hardware-friendly decompositions of, for instance, cardinality-constraint penalties and the fully connected XY-mixer, while maintaining rigorous performance guarantees compared to fully coherent implementations. Furthermore, we establish a formal connection between Fourier-based quantum penalties and Lagrangian relaxation, offering a unified perspective on constraint handling. We validate our approach using exact statevector simulations of 12-qubit circuits and large-scale experiments on 106 superconducting qubits. Our results illustrate how approximate sampling via an LCU systematically trades circuit complexity for sampling overhead, extending the practical reach of near-term quantum optimization.

Galois qudits are $q$-dimensional quantum systems whose choice of Pauli group encodes the arithmetic of some finite field $\mathbb{F}_q$. They differ from the more familiar modular qudit, which are the same quantum system but whose choice of Pauli group are the clock and shift operators, which encode the arithmetic of integer addition and multiplication modulo $q$. Galois qudits are a useful mathematical construct that allow us to leverage the mathematical tools that are native to the larger qudit while only physically building smaller qudits. In particular, a Galois qudit of dimension $q = 2^s$ is exactly the same thing as a collection of $s$ qubits, not only in its Hilbert space, but also in its Pauli group, and Clifford hierarchy. This formalism has found a lot of utility recently in constructing quantum error-correcting codes over qubits with useful properties. In this review, we build on existing literature to collect and formalise facts and proofs about Galois qudits over binary extension fields. We define them and their Clifford hierarchies, describe what it means to measure their Pauli operators, describe their stabiliser tableaux, formally define qudit-to-qubit mappings, and finally describe quantum Reed-Solomon codes.

We study the late-time behaviour of nonlinear perturbations of Schwarzschild-AdS$_4$ black branes and show that real-analytic initial data generically enter a regime controlled by the large-$k$ tail of the quasinormal mode spectrum $\{ω_{k\,n}\}$. Using the asymptotic scaling $\mathrm{Im}\,ω_{k\,n} \sim k^{-1/5}$ of the planar AdS$_4$ black brane, we derive a universal prediction that boundary observables decay in a stretched-exponential manner, specifically as $\exp(-c\, t^{5/6})$ up to a mild polynomial prefactor. Fully nonlinear numerical evolutions employing Fourier spectral and discontinuous Galerkin methods confirm this behaviour for small black holes and show consistent scaling - after suppressing long-lived low-$k$ modes - for larger ones. These results indicate that stretched-exponential decay with exponent $5/6$ is a robust feature of AdS$_4$ gravitational dynamics with real-analytic data, arising from geometric-optics physics rather than hydrodynamic modes.

A central open problem in quantum gravity is to understand how continuum spacetime emerges from quantum-geometric degrees of freedom in a background-independent setting. A many-body perspective suggests that spacetime emerges as a hydrodynamic phase of many atoms of quantum geometry. This idea underlies several approaches to quantum gravity, and it has been explicitly realised in the group field theory formalism. However, quantum fluctuations beyond the mean-field regime remain largely unexplored. We fill this gap by importing Bogolyubov theory to quantum gravity condensates, showing that leading beyond-mean-field effects manifest as collective excitations, in direct analogy with phonons in laboratory BECs. We implement the construction in a tractable group field theory model, where condensates of quantum-geometric atoms reproduce nonsingular expanding cosmologies, and derive the leading beyond-mean-field corrections to the emergent Friedmann dynamics. These results identify a new class of quantum-gravity excitations and establish a controlled bridge between microscopic quantum-gravitational dynamics, many-body collective phenomena, and signatures of spacetime emergence.

We calculate the two-point magnetic noise spectrum arising from Gaussian superconducting fluctuations, a quantity directly measurable by spin qubit pairs such as nitrogen vacancy centers in diamond. The analysis utilizes the time-dependent Ginzburg-Landau theory, reflecting the direct contribution of fluctuating Cooper pairs to the current correlations and consequent magnetic noise. We treat both two-dimensional systems and wires, considering them in equilibrium and under a uniform electric field. The signal is expected to be strongest in high-temperature superconductors, and we contrast our findings with the predicted signatures of a vortex liquid to offer an additional route to elucidate the nature of fluctuations in these systems.

We analyse two static spacetimes, recently generated from a Schwarzschild--Bertotti--Robinson electrovacuum seed through distinct symmetry transformations. The electromagnetic field of the transformed solutions can be set to zero and both solutions were presented as vacuum solutions of General Relativity. However, we show that once recast in Weyl form, both metrics are seen to be supported by a semi-infinite annular mass distribution on the equatorial plane. Thus, these metrics harbour a hidden matter source, visible in Weyl coordinates. In the original Schwarzschild-like coordinates equatorial null geodesics reach spatial infinity ($r\to\infty$) in finite affine parameter. In the Weyl representation, these geodesics approach the inner edge of the annulus, where the coordinate map degenerates.

Obtaining the symmetries of a model is a critical step towards developing an understanding and ultimately analytically or numerically solving the model. However, finding symmetries is generally extremely complicated, often being the result of insightful thinking. In this work, we complement human ingenuity with an algorithm. We leverage the classically efficient Clifford group to find symmetries for arbitrary many-body Hamiltonians via a graph representation. We demonstrate our method on random and physical Hamiltonians, with instances of up to one thousand qubits and demonstrate how our approach can provide deeper understanding of the model.

We study the relation between geodesic completeness, the averaged null energy condition (ANEC), and spatial curvature in Friedmann--Robertson--Walker (FRW) cosmology within classical general relativity. Using the affinely parameterized ANEC along radial null geodesics, we prove that non-static flat or open FRW spacetimes in the regular classes considered here cannot be both null geodesically complete and ANEC-satisfying. Bounded oscillatory or cyclic flat/open models do not circumvent the obstruction: the negative affine-ANEC bulk term accumulates over infinitely many cycles, giving \(I_{\rm ANEC}=-\infty\) for non-static periodic cases. Equivalently, within these classes, non-static ANEC-satisfying flat or open models are null incomplete. The sign obstruction is absent in closed \((k=+1)\) FRW geometry, where the positive curvature term enters the affine ANEC identity with the opposite sign and can support nonsingular, geodesically complete cosmologies with ordinary NEC-respecting matter. We give explicit closed-FRW scalar-field constructions, including a fully analytic quadratic reconstruction and a cubic-root reconstruction in closed quadrature, and contrast them with their flat realizations, which require NEC-violating support. Furthermore, we quantify how positive curvature can bias flat-model reconstructions toward an effective phantom equation of state, finding only a percent-level effect under current curvature bounds. The result is a curvature classification of ANEC-compatible eternal FRW cosmology: flat and open branches are obstructed, while the closed branch admits explicit complete realizations, with global de Sitter appearing as the vacuum limiting representative.

We review well known classical energy conditions and their implications for gravitational solutions, including the celebrated Hawking and Penrose singularity theorems. We then consider quantum fields coupled to gravity, where the topic becomes both richer and more subtle, as even the simplest quantum theories violate local energy conditions. We discuss directions for constraining energy densities in quantum field theories, including averaging over regions of spacetime and bounds relating energy and quantum information. We explore implications of these bounds for quantum field theory coupled to gravity as an effective theory and discuss how they guide our understanding of quantum gravity more broadly. These notes are based on a series of lectures given at the XXI Modave Summer School in Mathematical Sciences.

We introduce and explicit Calderbank-Shor-Steane (CSS) code construction that generalizes the Layer codes to $D=4,5$ dimensions. Much like its predecessor, the present construction is based on embedding quantum low-density parity check (qLDPC) codes; from an $[[n,k,d]]$ code with energy barrier $Δ$, we obtain a $D=4,5$ dimensional Layer code with parameters $[[Θ(n^{D/(D-2)}), k, Θ(dn^{1/(D-2)})]]$ and energy barrier $Ω(Δ)$. Using good qLDPC codes as input, our construction saturates the $D=4,5$ dimensional BPT bounds exactly. The higher dimensional Layer Codes are modular, and thus well suited to architectures composed of modular network patches, despite our physical limitation to three dimensions. We overcome the hurdles encountered by previous generalization attempts through the use of \textit{color routing}, allowing us to resolve the structure of the check layers and line defects.

The reliable generation of multi-qubit entanglement is a prerequisite for large-scale quantum information technologies. In particular, W states are a valuable resource owing to their resilience under local loss or measurement. Nevertheless, preparing these states with sequential two-qubit gates often requires substantial time overhead. By contrast, engineered simultaneous interactions enable fast entanglement generation, even in qubit systems with limited nearest-neighbour connectivity. Here, we demonstrate a set of fast and robust operations for coherently distributing a single excitation across a lattice of arbitrary size, thereby directly generating W states from initial product states. In 2D lattices, the excitation propagates along both directions simultaneously, such that the total entanglement time scales only with the largest dimension. We exploit this property to prepare a six-qubit W state in a 3$\times$2 superconducting lattice within 99 ns, achieving a tomographic fidelity of 83.9$\pm$1.0%. We then extend the protocol to create entanglement across chains of up to seven qubits, with the largest W state generated in 264 ns with a fidelity of 79.6$\pm$1.3%.

Objects subjected to a constant force generally increase their velocity over time. This expectation fails whenever their energy is a smooth and periodic function of momentum, resulting in periodic Bloch oscillations instead. Periodic dispersions, typical of lattice systems, can also emerge in continuum media through strong interactions. Here, we study the phenomenon of such Bloch oscillations in the absence of a lattice in a paradigmatic model of integrable quantum gases: the two-component Yang-Gaudin model. We derive a generalized-hydrodynamic theory of Bloch oscillations for a finite density of impurities embedded in a homogeneous interacting background, which we show to persist superimposed to a drift due to the acceleration of the center of mass. Moreover, we show the single-impurity oscillation period is renormalized at finite impurity density when two-magnon bound states are populated. Our results are relevant for ultracold atom experiments, where impurities can be created at controllable densities.

Proca and Maxwell fields in $d$-dimensional Schwarzschild black holes with anti-de Sitter (AdS) asymptotics are investigated through their linear perturbations and associated quasinormal modes (QNMs) with Dirichlet boundary conditions at infinity. The Proca field equations reduce to one decoupled and two coupled radial wave-like equations. We demonstrate how the Maxwell equations emerge from the zero-mass limit of the Proca system. Several analytical properties of the corresponding QNM spectrum are examined. To compute the QNM frequencies, we employ two complementary numerical methods particularly suited to asymptotically AdS spacetimes. Using these techniques, we determine the QNMs modes of Proca field perturbations in $4$, $5$, $6$, and $7$-dimensional Schwarzschild-AdS backgrounds. As a new result, we find numerically that scalar-type Maxwell perturbations in large $d\geq 5$ Schwarzschild-AdS black holes exhibit purely imaginary low-frequency modes, analogous to those found in vector-type gravitational perturbations. The presence of such modes is especially relevant within the AdS/CFT correspondence, as they correspond to the linearized hydrodynamic regime in the dual conformal field theory. We also analyze the influence of the Proca mass on the QNM spectrum, also emphasizing how Maxwell modes are recovered in the massless limit. The dependence of the spectrum on the black hole radius is explored. In addition, analytic expressions for the QNM frequencies of vector-type and monopole Proca perturbations, as well as Maxwell modes, are derived for small $d$-dimensional Schwarzschild-AdS black holes by matching asymptotic expansions using an intermediate region. These analytic results show good agreement with the numerical findings, confirming, in particular, the existence of purely imaginary low-frequency scalar-type Maxwell modes in large $d\geq 5$ Schwarzschild-AdS spacetimes.

The complexity of simulating quantum many-body dynamics, or quantum computations, in the Heisenberg picture is governed by the scrambling of initially simple operators into superpositions of exponentially many Pauli strings. The corresponding expansion coefficients define the Pauli spectrum, whose structure controls the performance of classical algorithms based on truncating Pauli expansions. Here we determine the finite-depth Pauli spectrum of random quantum circuits, both in the noiseless case and in the presence of local noise, through its moments, given by the operator stabilizer Rényi entropies. In noiseless circuits, we uncover a hierarchy in the approach to the fully scrambled regime: low moments equilibrate at relatively short depths, while higher moments, which are sensitive to rare, large-amplitude Pauli coefficients, require parametrically larger depths. In noisy circuits, scrambling competes with an effective suppression of operator spreading. Above a critical error per cycle $γ_c N=\mathcal{O}(1)$, the operator fails to reach the fully scrambled distribution and remains supported on an atypically sparse subset of Pauli strings. Conversely, below this scale, we rigorously show that classical simulation remains exponentially hard, demonstrating that finite noise does not automatically imply classical simulability. The resulting noise-induced transition in operator complexity therefore delineates the boundary between intrinsically hard quantum dynamics and those that remain classically accessible.

We propose a covariant holographic c-function, defined directly in a top-down background and constructed from the extrinsic curvature of codimension-two slices of the bulk geometry. The definition does not rely on a special choice of coordinates or on the existence of a consistent dimensional reduction. We show that it unifies previous foliation-based holographic c-functions into a single covariant formula, reducing to them in the appropriate limits. We evaluate the covariant expression in a range of top-down string backgrounds, including conformal models, confining geometries, flows across dimensions, and the Klebanov-Murugan geometry, in which the holographic radial direction mixes with internal coordinates and which is not the uplift of a lower-dimensional solution. In all cases, the c-function behaves as expected: it interpolates monotonically between AdS fixed points when they are present and decreases towards zero in gapped infrared regions, while in the Klebanov-Murugan case we recover the correct fixed-point values and find evidence for monotonicity. We highlight open conceptual issues, including: the lack of a universal covariant definition of the holographic radial direction in the presence of a nontrivial internal manifold; the derivation of the flow from a bulk action; and the relation to the entanglement c-function.

We present a non-perturbative method through which local probes can access the two-point function of a quantum field within a region of spacetime. By considering a lattice of gapless particle detectors, we identified the probe observables that encode the field's two-point function. We quantify the discrepancies introduced by physical finite-sized interaction regions by performing a spacetime multipole expansion of the smeared two-point function. Our protocol expresses the two-point function entirely in terms of measurable detectors correlations, providing an operational notion of states in QFT.

Resistive transport near a superconducting phase can arise from the motion of normal-state quasiparticles or that of vortices. The conductivity alone does not distinguish between these mechanisms. We propose an unambiguous method for telling them apart, using the recently developed experimental tool of covariance magnetometry, which uses nitrogen-vacancy centers in diamond to probe real-time spatiotemporal correlations in magnetic noise. Our key insight is that, under an applied current, the underlying charge carriers leave a directional fingerprint in the spatially correlated magnetic noise above the sample: ordinary electric carriers drift parallel to the current, whereas vortices, owing to the Magnus force, drift perpendicular to it. The noise covariance detects this anisotropy and identifies the vortex-driven nature of transport. We compute the noise correlations expected for a representative thin-film superconductor and demonstrate that the anisotropic signal is well within the reach of current experimental capabilities.

Orbital eccentricity in gravitational-wave signals from merging compact object binaries is a powerful indicator of their formation channel. Several binary black hole mergers and a neutron star--black hole merger have been reported to exhibit signs of eccentricity, but which events are identified and the significance of the eccentricity differs between studies. Measurements of eccentricity can change depending on the choice of prior. The choice of prior is subtle: eccentricity is commonly measured at an arbitrary reference frequency, which varies from study to study. We use the candidate eccentric neutron star--black hole merger GW200105_162426 as a case study, employing a range of priors and reference frequencies, and find the results to be strongly prior-driven. We show that the varied results reported across different studies can be partially reconciled by accounting for the evolution of eccentricity with reference frequency. In order to make conclusive statements about eccentricity, we propose a detection statistic that does not depend on reference frequency, and which marginalises over astrophysically-motivated distributions in eccentricity. Using this detection statistic, we find reduced support for the eccentric hypothesis for GW200105_162426: we obtain a natural log Bayes factor ln B $\leq$ 0.9 comparing the eccentric, aligned-spin hypothesis to the quasi-circular, precessing hypothesis. Our results cast doubt on the eccentric interpretation of GW200105_162426 and underscore the importance of modelling the astrophysical distributions of eccentricity in nature.

Penrose's original heuristic for his eponymous spacetime inequality -- a conjectured lower bound on the ADM mass in terms of the area of a horizon cross-section -- relies on the black hole final state conjecture. In this paper we isolate a substantially weaker but precise late-time condition, which we call the quasi final state hypothesis and prove the spacetime Penrose inequality under this hypothesis. More precisely, for an asymptotically flat globally hyperbolic spacetime with a black-hole-type apparent horizon tube ${H}_{app}$ satisfying the dominant energy condition and the quasi final state hypothesis, we show that every asymptotically flat initial data set whose boundary is a MOTS cross-section of ${H}_{app}$ satisfies the spacetime Penrose inequality. The quasi final state hypothesis requires only a late-time decay condition on the normal component of the shift and the ratio of timelike to spacelike mean curvature, together with convergence of the cross-sectional areas of ${H}_{app}$ to a finite limit. Our approach is new and formulated directly in spacetime. The main geometric object is what we call a \emph{tangentially maximal} hypersurface, carrying a foliation by spacelike spheres whose timelike mean curvature vanishes. We show that these hypersurfaces are governed by a quasilinear inward-parabolic PDE, and we develop the corresponding a priori theory and prove global existence. On these hypersurfaces, the spacetime Hawking mass reduces to the Riemannian Hawking mass, and the dominant energy condition gives nonnegative scalar curvature. The Riemannian Penrose inequality, combined with the area laws for dynamical and isolated horizons, then yields the result.

The asymptotic rates of information-theoretic protocols - including error exponents, compression rates, and channel capacities - are traditionally defined under the idealised assumption that the underlying resource (state or channel) is independent and identically distributed (i.i.d.). Somewhat surprisingly, even slight departures from the exact i.i.d. structure can lead to a drastic breakdown of these protocols. The asymptotic rates of information theoretic protocols - error exponents, compression rates, capacities - were originally evaluated taking for granted that the underlying source (state or channel) is i.i.d. Differently from what we might expect at first glance, it is not hard to exhibit instances of protocols that may drastically fail when the i.i.d. assumption holds only approximately rather than exactly. If the precise nature of the perturbation from the i.i.d. regime is known (e.g. a pointwise defect), we could design a bespoke protocol that compensates for the defect (for example, by discarding the corrupted subsystem). However, in any realistic setting, neither can the i.i.d. behaviour of the system be precisely guaranteed, nor can the deviations from the ideal regime be determined exactly. In this paper we answer the following question: are there protocols that can still achieve the optimal asymptotic rates when the i.i.d. resource is replaced by any arbitrary almost i.i.d. resource along it? What is the nature of the unknown perturbation under which protocols like these are possible? We focus, in particular, on hypothesis testing, data compression, and channel coding. As a by-product of our analysis, we introduce the notion of club distance, as a variant of the well-known diamond distance, and of an almost i.i.d. process, which may be of independent interest.

We address a fundamental question: Can one determine whether a received signal is squeezed when each copy arrives with a different displacement/amplitude? We introduce an interaction Hamiltonian that converts quadrature squeezing into number squeezing. Using this conversion, we test whether the copies satisfy $g^{(2)}(0)<1$. The Hamiltonian itself does not create nonclassicality; it only transfers it from quadrature squeezing to number squeezing. This allows us to identify squeezing even when individual copies have random displacements.

Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based quantum simulators. These devices generally generate data in the form of images; we first showcase denoising of raw images and then identify solitonic waves in Bose-Einstein condensates. In both of these examples, we comment on the interplay between performance, model complexity, and interpretability.

The distribution of entangled states is a core task for quantum networks facilitating quantum communication, and the use of multipartite entangled states comes with its own set of considerations. In this work, we analyze a quantum conference agreement protocol based on GHZ states in a network with a central station to which multiple clients are connected. Using comprehensive numerical simulations, we investigate how minor variations in the scenario-such as the number of parties, the number of memories, and asymmetric distances from the central station-can drastically influence the performance of the protocol. In particular, we demonstrate that it is crucial to adjust the strategy by optimizing cutoff times. From a broader perspective, we argue that numerical simulations are an indispensable tool for protocol design for devising realistic schemes for quantum communication.

The Jaynes-Cummings (JC) interaction-the coherent exchange of excitations between a two-level system and a harmonic oscillator-is one of the fundamental interactions of quantum optics, realized across platforms such as cavity quantum electrodynamics, trapped ions, mechanical resonators, and superconducting circuits. Although JC interactions and qubit rotations form a universal gate set for oscillator control, practical implementations have not been demonstrated. Here we develop and experimentally demonstrate universal JC-based oscillator control by compiling arbitrary unitary gates into sequences of JC interactions and qubit rotations. In our experiment, the oscillator is realized using a mode of a high quality factor microwave cavity and the ancilla qubit using a superconducting transmon circuit, with the JC interaction implemented by a sideband interaction enabled by the Josephson nonlinearity. The native gates are constructed to be closed below a chosen cutoff photon number, encoding a qudit with suppressed leakage errors, while ancilla relaxation errors are detectable. We further find that the dispersive shift serves as a compilation resource that reduces circuit depths. We demonstrate universal qudit control and implement a single-qutrit gate set with a mean post-selected process fidelity of 96%, as well as ququart and ququint shift gates. These results establish Jaynes-Cummings control as a practical route to universal oscillator control, enabling programmable bosonic processors across a variety of quantum platforms.

The Reeh-Schlieder theorem says that every target vector can be approximated from the vacuum by an operator localized in an arbitrarily small spacetime region, but it gives no quantitative cost for doing so. This note isolates a standard Tomita-Takesaki estimate as a model-independent preparation bound. Targets with deeply negative modular energy require large local operators. After rescaling such an operator to a physical contraction, the same estimate becomes a lower bound on postselection overhead. In geometries where the modular Hamiltonian is known, the bound becomes explicit. Bisognano-Wichmann turns it into a boost energy statement for wedges, and the Casini-Huerta-Myers formula gives a stress-tensor version for bounded regions of conformal field theories. Local unitaries can only reach states of nonnegative modular energy. Negative modular sectors require nonunitary or postselected outcomes, giving a preparation cost bound that complements vacuum embezzlement in type III local algebras.

The requirement of complete positivity is very often regarded as a fundamental consistency condition for the description of open quantum dynamics. We critically examine this requirement and discuss both its physical motivations and its limitations. We analyze proposals based on restricting the domain of non-completely positive maps to subsets of compatible initial states. Using isotropic states as a concrete example, we show that such domain restrictions become increasingly severe with growing system dimension, revealing an intrinsic weakness of the compatibility-based approach.

In this paper, we investigate the Holographic Dark Energy (HDE) model and its entropy-corrected versions, namely the Power Law and Logarithmic entropy corrected HDE models, by considering the infrared cut-off $L=\mathcal{G}^{-1/4}$, where $\mathcal{G}$ is the Gauss-Bonnet invariant. We derived the Equation of State parameter $ω_D$, the deceleration parameter $q$ and the evolutionary form of the fractional energy density of DE $Ω_D'$ for flat and non-flat universes, with and without interaction between DE and Dark Matter. We also analyzed the asymptotic behavior in the DE dominated epoch. Furthermore, correspondences between the considered HDE models and several scalar field models, including tachyon, k-essence, quintessence, Generalized Chaplygin Gas, Yang-Mills, and Nonlinear Electrodynamics models, were established.

We study the near-kernel sector of the Hamiltonian constraint operator in the one-vertex model of quantum reduced loop gravity using variational Monte Carlo methods with neural quantum states. The analysis is based on the symmetric Hamiltonian containing both Euclidean and Lorentzian contributions, and on the variational minimization of the positive quadratic operator $\hat{\mathcal Q}=\hat C \hat C^\dagger$ in truncated Hilbert spaces with spin cutoff up to $j_{\mathrm{max}}=1001$. The resulting near-kernel states are found to organize into three qualitatively distinct classes. At low cutoffs, we find solutions that do not factorize across the three edge degrees of freedom. At larger cutoffs, we find two different factorized branches, both described to very high accuracy by products of one-edge wavefunctions but localized in different spin regimes. One of these branches is matched with near-unit fidelity by reduced Thiemann coherent states, providing evidence for an emergent semiclassical organization of the near-kernel sector. The other is likewise strongly factorized, but its one-edge factors are not well described by the same coherent-state family.

Quantum many-body scars provide a controlled form of weak ergodicity breaking, in which structured nonthermal eigenstates coexist with a thermalizing many-body spectrum. We introduce a qubit-level route to exact scars based on the intrinsic soliton structure of the Rule-54 quantum cellular automaton. A hard-core dimer sector of Rule 54 supplies an exactly translatable protected skeleton, while local projector-controlled decorations are invisible on this skeleton and nontrivial outside it. The protected dynamics is therefore reducible to finite translation orbits, whose Fourier modes form exact Floquet eigenstates with sub-volume-law entanglement. The number of exact scars grows with Fibonacci combinatorics, whereas their fraction in the full qubit Hilbert space remains exponentially small. Finite-size simulations show Page-like eigenstate entanglement, rapid entanglement growth, fidelity decay, and circular unitary ensemble quasienergy statistics in the decorated complement. This construction demonstrates that exact many-body scars can be engineered from native finite-orbit structures of an interacting reversible automaton, and provides a direct starting point for digital quantum simulation of scarred cellular-automaton dynamics.

Non-Gaussian entanglement is a promising resource in various quantum tasks. A recently defined class identifies entanglement that cannot be generated by applying Gaussian operations to separable inputs. To further explore the entanglement in this context, we introduce a quantitative witness $E_{\rm NG}$ in bipartite bosonic systems, which satisfies $E_{\rm NG}=1$ for all Gaussian-entanglable states, while $E_{\rm NG}>1$ certifies non-Gaussian entanglement. Its ceiling $d=\lceil E_{\rm NG}\rceil$ provides a lower bound on the Schmidt number irreducible by Gaussian transformations, thereby defining a natural hierarchy of non-Gaussian entanglement. For pure states, the condition is sharp and the hierarchy reflects the complexity of state learning. We benchmark the framework with some paradigmatic non-Gaussian states, such as NOON states and squeezed Kerr states, and analyze its robustness against loss. Moreover, we construct an experimentally economical NOON-type witness requiring only four projective measurements, where an analytical Gaussian-entanglable threshold is derived. These results establish an operationally meaningful and experimentally accessible framework for identifying non-Gaussian entanglement resources in continuous-variable quantum platforms.

As gravitational-wave ringdown signals grow louder, quasinormal-mode inference depends increasingly on the treatment of extrinsic parameters. Standard analyses fix sky localization - and sometimes also polarization and inclination - to point estimates from a prior inspiral-merger-ringdown analysis, artificially breaking degeneracies and underestimating the true uncertainty of mode-amplitude values. We test two alternatives: uninformative priors on the extrinsic parameters, sampled jointly with the remnant mass, spin, mode amplitudes, and phases; and informed priors on sky position from the full signal posterior. The former yields wider marginal constraints on amplitude posteriors, and both avoid potential bias introduced by fixing the sky localization. In contrast, mode amplitude ratios remain consistent across approaches, making them a robust observable for Kerr spectroscopy. Our publicly available pipeline enables fast ringdown analyses capable of sampling all parameters, requiring tens of minutes on a laptop for a full inference. Applied to GW250114 and GW190521, our methods confirm the robust detection of the $(2,2,1)$ overtone in GW250114, and, for GW190521, find only mild evidence for the $(3,3,0)$ mode.

We investigate Krylov spread complexity for the ground state of two-band Hamiltonians, where the reference state is a generic state on the Bloch sphere. The spread complexity is obtained by using a purely geometric formulation in terms of Bloch sphere data without constructing the circuit Hamiltonian. For generic reference states, the derivative of the spread complexity is logarithmically divergent at the topological phase transition in the Su-Schrieffer-Heeger (SSH) model. We demonstrate that the derivative of the spread complexity is bounded by fidelity susceptibility for general two-band models, indicating the sensitivity of the spread complexity to any gap closing (topological or trivial). This is illustrated in the massive Dirac Hamiltonian with a trivial gap closing. Finally, we introduce a non-unitary duality in the SSH model between the topological and trivial phases, which manifests itself in the spread complexity and fidelity susceptibility.

The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time $t$, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths.

We develop a geometric realization of a broad class of generalized black hole entropy functionals by establishing their direct correspondence with the Misner$-$Sharp quasilocal mass and the Wald Noether$-$charge entropy in scalar$-$tensor theories of gravity. The resulting models feature a scale-dependent effective gravitational coupling, whose functional dependence is determined by the underlying entropy parameters. Within this framework, we derive explicit Einstein-frame scalar potentials: for Barrow entropy, a steep exponential potential; for Tsallis$-$Cirto entropy, an exponential potential governed by the nonextensivity parameter; and for quantum-gravity and entanglement$-$induced corrections, an approximately linear potential. These distinct potentials generate characteristic cosmological phenomenology, with implications for inflationary dynamics, late-time dark-energy behavior, and non-singular bouncing cosmologies. The framework is compatible with current constraints from solar-system tests, big-bang nucleosynthesis, and pulsar-timing observations, and it yields predictions that can be probed by forthcoming observational surveys. In this way, the analysis establishes a unified and geometrically grounded connection between information$-$theoretic entropy proposals and gravitational field theory.

We compute the gravitational impulse for two classical massive scalars in the ultrarelativistic limit to all orders in Newton's constant $G_N$ at fixed $G_N s/m b$ to $O(m^4/s^2)$. By computing the 4 and 5-point scattering amplitudes in the small-mass regime of $-t\sim m^2$, we are able to resum all large $G_N s$ corrections. Applying the KMOC formula for the impulse and taking the large mass limit, we recover the classical result at high energies. This resummation is in complete agreement with known results in the post-Minkowski expansion, and in the massless limit we recover previous results for the radiated energy. We use this resummed amplitude to predict the leading high-energy behavior of the PM expansion to eleventh post-Minkowski order.

Quantum machine learning (QML) has attracted considerable research interest, yet whether it offers practical benefits over classical approaches remains an open question. The choice of data encoding significantly influences QML performance, but why certain encodings outperform others remains poorly understood. We employ Monte Carlo Tree Search (MCTS) to discover optimal data encoding circuits for a quantum-classical convolutional neural network (QCCNN) combining a non-variational quantum block for feature extraction with a classical classifier. Evaluating on two medical imaging datasets, the discovered circuits outperform commonly used encoding strategies while showing competitive results compared to purely classical counterparts. We further analyze metrics to identify predictors of encoding performance. Entanglement capability and Fourier decomposition provide minimal insight, whereas the effective rank of the feature maps exhibits meaningful correlation and can serve as a threshold criterion to accelerate the search for high-performing encodings. Our findings provide both a practical method for encoding discovery and new insights into what makes data encodings effective in QML.

Quantum computers are increasingly powerful. Software tools for the development of quantum-enhanced algorithms are maturing. However, the software stack still lacks the connection to applications that would enable hybrid algorithms combining classical and quantum computing steps. End users need to be assisted in choosing the best combination of preprocessing, postprocessing, classical and quantum algorithms options. The application-facing software stack is therefore required to cover problem modeling, encoding, algorithm selection and hyperparameter tuning. A variety of tools exist for specific recommendations. The QuaST Decision Tree reflects the complexity in combining individual decisions in its modular network structure, consisting of flexible computation nodes with modular recommendations. It can easily be configured to serve in an industrial solver, an HPC software stack, or for rapid prototyping in development. The key ingredient, automation, is delivered by modules. We present one such module judging the feasibility of variational algorithms based on a robust scalability analysis and classification of problem instances. The automation improves the performance of end-to-end solutions, highlights the benefit to be gained from the hybrid quantum solution, reduces expensive trial-and-error testing, and leads to an improved utilization of quantum devices for a practical benefit.

Controlling light photon-by-photon is central to quantum optics. At a fundamental level, photon interactions are mediated by their coupling to atoms, and ultimate control requires deterministic light-matter interfacing of single photons to single atoms. Extending this paradigm to radiatively couple multiple individual atoms in a deterministic and scalable manner opens the arena of many-body quantum optics. Here, we realize such a setting by coherently coupling solid-state artificial atoms to a nanophotonic waveguide and demonstrate higher-order photon correlations that are controlled by the number of quantum emitters. We study the scaling of nonlinear photonic transport induced by emitter-photon scattering and demonstrate that adding a quantum emitter generates higher-order photon correlations. Specifically, we experimentally observe genuine three-photon correlations from a pair of collectively coupled emitters, while contributions from lower photon numbers are suppressed. In addition, we scale to three resonant quantum emitters coupled to the waveguide. These advancements demonstrate the onset of many-body quantum optics in waveguide quantum electrodynamics, enabling new photonic quantum simulators, the creation of many-body entangled states, and the exploration of novel quantum phase transitions.

Low-dimensional materials have emerged as promising hosts for quantum emitters, whose emission typically arises from either strain-induced band bending or defect-induced two-level systems. Among these materials, transition metal dichalcogenide (TMD) monolayers have attracted particular attention; however, their performance is limited by strong photoluminescence (PL) quenching at room temperature. As TMDs transition from a direct to an indirect bandgap when moving from monolayers to multilayers, we herein propose a strategy to overcome this quenching limitation by exploiting the indirect bandgap of TMD bilayers in combination with a point defect doping. The indirect gap suppresses excitonic PL, while specific defects enable robust defect-mediated quantum emission. Using hybrid-functional density functional theory, we investigate substitutional carbon defects at chalcogen sites (S and Se) in WS2, WSe2, MoS2, and MoSe2 bilayers and comprehensively characterize their optical properties. Both neutral and singly negative charge states are found to be thermodynamically stable. Neutral defects exhibit singlet configurations with emission in the O- and C-band telecommunication windows, whereas negatively charged defects adopt doublet configurations featuring spin-selective transitions and near-infrared emission. The electron-phonon coupling strength, radiative lifetime, and dipole orientation are found to depend sensitively on both the host material and defect site, providing distinct fingerprints for experimental identification. Our findings, therefore, establish carbon-doped TMD bilayers as promising platforms for room-temperature defect-based quantum emitters operating at telecommunication wavelengths.

We study the non-stabilizerness (quantum magic) content of the Hubbard dimer, an analytically solvable, yet completely non-trivial, model of strongly correlated fermions. We can access zero- and finite-temperature properties as well as the time evolution in a quantum quench protocol. We evaluate local and nonlocal non-stabilizerness using both the robustness of magic and the stabilizer Renyi entropy, demonstrating how the latter often fails in detecting the mixed stabilizer states that are typically found in this kind of systems. Finally, we compare the non-stabilizerness with other genuine resources of quantum-state complexity, i.e., the fermionic non-Gaussianity and the superselected two-site entanglement. Our findings corroborate the notion of non-stabilizerness as a fundamentally different quantum resource, able to give profound insights that are missed by more traditional information-theoretic quantities.

In this work we reformulate the Uhlmann purification-overlap optimization and develop a purification-based geometric framework for the analysis of mixed qubit states and qubit channels. Using the Fano representation of two-qubit pure states, a purification is described in terms of the Bloch vector of the system, the ancilla Bloch vector, and a real correlation matrix. For a fixed one-qubit mixed state, the freedom in the choice of purification can be parametrized by proper rotations acting on the ancillary degrees of freedom. As a result, the optimization over purifications entering the definition of the metric \(D_N\) introduced in Ref.~\cite{Lamberti2009} is reduced to an orthogonal Procrustes problem on the Lie group \(SO(3)\). This reduction yields not only the maximal purification overlap, but also the optimal rotation relating the purification frames. From this rotation we define a purification misalignment angle \(Θ\), which provides geometric information not contained in scalar fidelity-based distinguishability measures. The formalism is applied to representative qubit channels, including depolarizing, bit-flip, phase-flip, amplitude-damping channels, and an imperfect quantum NOT gate. For symmetry-adapted evolutions preserving the Bloch-vector direction, the optimal rotation is trivial and \(Θ=0\), whereas noncollinear channel actions generate a nonzero misalignment. The pair \((D_N,Θ)\) therefore separates the magnitude of the maximal purification overlap from the geometric reorientation of the optimal purification frames. Since the optimal Procrustes rotation can be lifted to a local unitary acting on the ancilla, the construction also provides an operational interpretation of the optimal purification in terms of an ancilla-side transformation.

This note summarizes the physics and mathematics of Lorentz transformations for massless particles, specifically for photons. We provide a complete analytical derivation of Wigner's little group matrix and a closed formula for the calculation of Wigner's angle for arbitrary Lorentz transformations. Our derivation highlights the geometrical content of the sequence of little group transformations leading to Wigner's matrix and links it to classical theorems in spherical trigonometry.

Gravitational-wave observations provide a powerful probe of compact objects and strong-field gravity. In this work, we investigate the detectability of binaries containing (sub-)solar-mass black holes and superspinars with current and next-generation gravitational-wave observatories. Such objects may arise from primordial formation channels or from more exotic high-energy scenarios, and their detection would provide important insights into the population of low-mass compact objects and the physics of extreme gravitational fields. We model the gravitational-wave signals using the frequency-domain post-Newtonian inspiral waveform model TaylorF2, and truncate the signal at the innermost stable circular orbit (ISCO) to avoid contamination from the post-inspiral regime. We assess the observability of these systems using the sensitivities of current detectors such as Advanced LIGO and upcoming third-generation observatories including the Einstein Telescope and Cosmic Explorer. Our results show that while current detectors have limited reach for very low-mass binaries, third-generation observatories can enhance both detection capability and parameter-estimation precision. Their improved strain sensitivity and extended low-frequency coverage allow these observatories to track the inspiral phase over a substantially larger number of gravitational-wave cycles. As a result, they achieve considerably higher signal-to-noise ratios and provide dramatically improved constraints on binary parameters. In particular, it is possible to measure the primary spin parameter with precision $Δχ_{1z}~\sim~10^{-4}-10^{-3}$, potentially allowing clear observational discrimination between near-extremal black holes and superspinars in the mass range $0.1~M_\odot-2~M_\odot$ and with signal-to-noise ratio of $\sim 100-350$.

We study scalar field theories that break diffeomorphism invariance down to the subgroup of transverse diffeomorphisms through the matter sector in cosmological backgrounds. We focus on single- and multi-field models and develop the corresponding cosmological perturbation theory. We analyze the different contributions to the pressure perturbation, discussing the adiabaticity and the effects in the perturbation coefficients of the interactions that arise in the multi-field case as a consequence of the symmetry breaking. We also consider the stability of the perturbations in terms of the effective speed of sound and present particular models that could be of phenomenological interest.

Low-energy effective theories provide a natural description of four-dimensional physics in higher-dimensional geometries, where the imprint of the bulk geometry appears as parameters of the lower-dimensional theory. Inspired by the Damour-Esposito-Farése (DEF) model of spontaneous scalarization in first generation Scalar-Tensor theories of gravity, we investigate the possibility of tachyonic instability and spontaneous scalarization in braneworld scenarios. We consider the two-brane Randall-Sundrum model where the low-energy effective theory on either brane is of scalar-tensor nature with the extra-dimensional radion playing the role of the scalar. We have determined the possibilities for tachyonic (in)stability of the radion field on either branes in three scenarios: the Randall-Sundrum (RS) model with fine-tuning conditions in which the potential of the radion field vanishes identically, the RS model without fine-tunings where the radion potential arises purely from the gravity sector, and the RS model with a bulk stabilizing field that generates a radion potential with a minimum. With the bulk stabilizing field, we have found that on-brane matter with $T>0$ changes the VEV of the radion, destroying the resolution of the gauge hierarchy problem, whereas on-brane matter with $T<0$ does not alter the stability and VEV of the radion. We further determined the exact condition of tachyonic (in)stability of radion field on Planck brane with the FLRW background geometry on the brane. Finally, we have extended the same formalism to study the tachyonic (in)stability conditions of $f(R)$ theories of gravity with the motivation that the 4D effective theory on RS branes can be casted into a $f(R)$ theory on the branes at the inflationary epoch of cosmology.

We investigate the task of conference key agreement in near-term quantum networks, where the nodes are connected by sources of bipartite entangled states, under the class of local operations not requiring quantum memory. We derive upper bounds on the distillable conference key depending on the network topology and degree of entanglement of the sources, and prove tightness of these bounds for some particular cases. In these cases, we show that pairwise bipartite key distillation followed by merging the bipartite keys into the conference key is optimal.

Driven-dissipative quantum systems can exhibit robust transport and amplification in topological regimes, yet the connection between topology and the extent of correlations remains largely unexplored. In this work, we develop a general framework that links topological phases in driven-dissipative systems to bosonic correlations via the singular value decomposition (SVD). In essence, we claim that non-Hermitian topology in quadratic Liouvillians is directly encoded in steady-state correlations, providing an intrinsic characterization of topology without external probes. We show that topological amplification induces disorder-robust long-range order (LRO) in steady-state correlations at fixed frequency, establishing frequency-resolved correlations as direct signatures of non-Hermitian topological phases. We introduce a vector-valued topological invariant that captures the total number of singular-value gap closings across the frequency axis, extending the concept of adiabatic deformation from topological insulators to the case of topological phases of quadratic Liouvillians. Within this framework, we further demonstrate that the spatial structure of equal-time correlations encodes global topological information, manifested as a Gaussian spatial decay with distance in the topological phase, in contrast to the exponential decay characteristic of trivial phases. These findings open new avenues for quantum sensing and correlation engineering in non-Hermitian systems, with feasible implementations in platforms such as trapped ions and superconducting circuits.

This paper proposes a quantum algorithm for the capacitated vehicle routing problem with time windows (CVRPTW) based on Grover Search framework. This problem is often faced by Postal services in the context of package delivery or other time-sensitive operations. We provide an implementation on gate based quantum computer of a model inspired by classical route first, cluster second technique. The quantum paradigm allows to overcome suboptimality inherent property of this decomposition. In the current NISQ (Noisy Intermediate-Scale Quantum) era, the most important limitation is the number of available qubits which makes time windows and capacity constraints hard to tackle. We introduce a qubit-efficient split-inspired modeling which adds only a linear number of decision qubits to standard quantum formulations for Traveling Salesman Problem (TSP).

We derive ultimate precision bounds for estimating parameters encoded in \emph{time-dependent} Hamiltonians in the presence of general Markovian noise, allowing for arbitrary adaptive protocols with fast controls and noiseless ancillas. Extending the minimization-over-purifications framework to time-varying continuous channels, we obtain a differential upper bound on the achievable quantum Fisher information (QFI) that can be evaluated at all times via semidefinite programming. For parameter-independent noise, we prove a universal long-time scaling law: if the coherent (noiseless) dynamics yields $Q_{\mathrm{coh}}(T)\sim T^{2k}$, then under Markovian noise the QFI scales at most as $Q(T)\sim T^{2k}$ in the DHNLS regime, whereas in the DHLS regime it is fundamentally limited to $Q(T)\sim T^{2k-1}$. We illustrate these behaviors on paradigmatic driven-qubit sensors, exhibiting $T^{4}$ and $T^{3}$ scalings under dephasing and spontaneous emission, respectively. Finally, we provide explicit continuous exact and approximate quantum error correction constructions -- supplemented by spin-squeezed probes -- that asymptotically saturate the bounds, establishing their tightness.

We investigate the effectiveness of the stabilizer Rényi entropy (SRE), a quantifier associated with non-stabilizer resources (quantum magic), as an indicator of quantum phase transitions. Specifically, we analyze the behavior of the purity-corrected SRE of reduced density matrices in the ground states of two one-dimensional spin models: the transverse axial next-nearest-neighbor Ising (TANNNI) model and the quantum compass model (QCM). The ground state of the TANNNI model is obtained using exact diagonalization techniques, while the QCM is analyzed using the Jordan--Wigner (JW) transformation followed by Bogoliubov diagonalization of the resulting quadratic fermionic Hamiltonian. For the TANNNI model, the purity-corrected SRE successfully detects the antiphase--floating phase transition in the high-frustration regime, while in the low-frustration regime the raw (purity-uncorrected) SRE reproduces the known ferromagnetic--paramagnetic phase boundaries more accurately. For the QCM, the purity-corrected SRE exhibits a clear signature near the isotropic point \(J_x/J_z=1\), where the system undergoes a first-order quantum phase transition. Our results establish SRE of reduced states as a complementary probe of quantum criticality and provide further insight into the role of non-stabilizer resources in many-body quantum phase transitions.

Fractional Chern insulators (FCIs) are lattice analogs of fractional quantum Hall systems, where the interplay of strong interactions with a frustrated tunnelling kinetics leads to the emergence of a gapped ground state with long-range entanglement and anyonic excitations. The highly correlated nature of such systems makes their adiabatic preparation challenging already beyond the minimal system size of two particles. Considering Floquet implementations of the bosonic Harper-Hofstadter-Hubbard model of few photons in superconducting circuits, we design protocols for the driven-dissipative stabilization of its FCI ground state at half filling via quantum bath engineering. Dissipation control is achieved through the coupling to driven leaky cavity modes, which realize a tuneable artificial environment having the Floquet-FCI as its approximate fixed point. For systems of two, three and six particles, we show numerically how the flexibility of the control scheme further allows for the detection of fractional quantum Hall signatures in the stabilized steady states, including bulk incompressibility, Hall response and the trapping of fractional charges. Our results provide a concrete pathway to dissipation-assisted preparation of strongly correlated states in quantum simulators.

In this paper, we explore wormhole solutions in a higher-derivative theory of gravity where the action depends not only on the Ricci scalar \(R\), but also on its d'Alembertian, \(\Box R\). Such \(f(R,\Box R)\) models are motivated by quantum corrections to general relativity and naturally extend the space of possible gravitational geometries. Our goal is to examine whether traversable wormholes can exist in this framework and to understand the role of higher-order curvature terms in supporting them. We derive the field equations for a static, spherically symmetric wormhole and study their solutions using both analytical arguments and numerical methods. Particular attention is given to the classical energy conditions, which are usually violated in wormhole physics. We find that the higher-derivative corrections can effectively contribute to the stress-energy tensor, reducing the amount of exotic matter required at the throat, and in some cases eliminating the need for it altogether.

By introducing a nonminimal coupling between the Kalb--Ramond field and the electromagnetic field, we construct an exact four-dimensional static, spherically symmetric dyonic black hole solution in Lorentz-violating gravity with a background Kalb--Ramond field. The curvature invariants show that the spacetime retains a genuine curvature singularity at $r=0$. We then analyze the geodesic motion of null and timelike particles and obtain the photon-sphere radius, the shadow radius, and the innermost stable circular orbit, demonstrating that both the Lorentz-violating parameter and the dyonic charges can appreciably modify the shadow size and the domain of stable circular motion. In the extended phase space, we derive the thermodynamic quantities and verify the first law of black hole thermodynamics together with the Smarr relation. The system also exhibits a first-order phase transition between small and large black holes, and its phase structure is strongly influenced by the Lorentz-violating parameter and the dyonic charges.

Finite-time quantum heat engines (QHEs) typically extract less work than their quasistatic counterparts because fast driving generates coherences and non-adiabatic transitions during the work strokes, a phenomenon commonly referred to as quantum friction. Quantum lubrication denotes a broad class of strategies that use auxiliary systems or controls to mitigate this loss. In this work, we introduce a lubrication protocol based on the quantum Zeno dynamics (QZD). By coupling the working medium to an auxiliary lubricant system and frequently monitoring the lubricant, we confine the joint evolution to a Zeno subspace and obtain an effective shortcut to adiabaticity during the work strokes of a QHE running an Otto cycle. In the ideal Zeno limit, the protocol reproduces the transitionless dynamics required to preserve populations in the instantaneous energy basis and recover the Otto efficiency at finite stroke duration. We also analyze several implementation-dependent thermodynamic costs, including switching, driving, monitoring, and imperfect thermalization, in order to assess how these costs constrain the practical gains in efficiency and power. Our results identify QZD as a conceptually distinct route to quantum lubrication and highlight quantum heat engines as a useful setting in which to study the interplay between strong coupling, measurement, and quantum thermodynamic control.

Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with parameterized quantum circuits in an end-to-end trainable framework. However, their performance and efficiency depend strongly on architectural choices such as data encoding, circuit structure, measurement design, and the coupling between classical and quantum modules. This makes manual design increasingly difficult, especially when hardware limitations and resource constraints must also be taken into account. In this paper, we study the foundations of HQNNs and neural architecture search (NAS), discuss how NAS extends to quantum and hybrid settings, and demonstrate FLOPs-aware search (where FLOPs serve as a proxy for computational complexity), as an important hardware-aware direction for building HQNNs that are not only accurate but also computationally efficient and practically deployable.

Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings. This work adapts the Quantum Leaky Integrate-and-Fire (QLIF) spiking neural network for time-series regression tasks, specifically short-term multivariate weather forecasting. We extend QLIF beyond classification and demonstrate its applicability to continuous-valued prediction problems. The QLIF-CAST model encodes neuron excitation states as single-qubit quantum superpositions, driven by Rx rotation gates and T1 relaxation decay, and is embedded within a hybrid quantum-classical recurrent architecture. We conduct two distinct evaluations. First, a controlled comparison against a parameter-matched classical LIF baseline on a multivariate weather dataset shows that QLIF-CAST achieves 15.4% lower MSE and 4.4% lower MAE, demonstrating that quantum neuronal dynamics reduce prediction error over classical equivalents. Second, a cross-domain comparative analysis with state-of-the-art quantum LSTM (QLSTM) and quantum neural network (QNN) models on air quality and wind speed benchmarks reveals that QLIF-CAST converges in up to 94% less training time, occupying a distinct position in the speed-error trade-off space. Hardware verification on IBM Marrakesh (156-qubit QPU) confirms reliable circuit execution with only 1.2% average deviation from simulation.

The unpredictability of quantum physics gives rise to intrinsic randomness. In an adversarial scenario, any additional degrees of freedom must be attributed to an eavesdropper with correlations to the measurement set-up. The true randomness is then quantified by the probability that she correctly guesses the measurement outcomes, optimised over all possible strategies. Extremal measurements are appealing here, since they do not allow information to leak to such an eavesdropper. Beyond projective measurements, however, a simple question remains open: how much intrinsic randomness can be generated by a given extremal measurement? In a step towards solving it, we characterise the randomness generated by any unbiased extremal rank-one measurement acting on any state, solving the problem explicitly in dimension two. Four-outcome qubit measurements of this type are tomographic, so these results hold for fully source-device-dependent randomness too. The tetrahedral symmetric informationally complete (SIC) measurement, we find, has the least intrinsic randomness within this class. We also present the skewed SIC family of measurements, and use them to partially solve an open problem: we prove that $2 \log d$ bits of randomness, the maximal amount, can be generated device-dependently (or source-device-independently) in any dimension in which there exists a SIC measurement.

Nonlinearity is a key resource in both classical and quantum signal processing. Nonlinear nanomechanical elements have found applications ranging from sensing to computing, while networks of nonlinear resonators, as well as nonlinearly coupled networks of linear resonators, constitute promising platforms for simulating complex dynamics. Here, we experimentally demonstrate an approach to realizing strong mechanical nonlinearity in nanomechanical resonators, fully controlled through optical laser drives. The mechanism exploits the nonlinearity of the radiation-pressure interaction in a cavity optomechanical system, which gives rise to a nonlinear optical spring effect. The resulting Duffing nonlinearity is conveniently tunable in strength via pump laser power, while its sign is controlled by laser detuning. Moreover, we demonstrate that the nonlinear optical spring mediates effective interactions between mechanical modes coupled to a common cavity, inducing tunable nonlinear interactions between them that impact spectral response and dynamics. These results establish cavity optomechanics as a versatile and in-situ reconfigurable platform for engineering nonlinear dynamics in resonators and networks.

[Abridged] In slow roll inflation, the power spectrum (PS) of primary gravitational waves (PGWs) generated from the quantum vacuum rises as $k^2$ over wave numbers $k$ which never leave the Hubble radius. In fact, over such small scales, the PS exhibits a similar behavior at any time after inflation. In a recent work, we had argued that the PS of PGWs has to be regularized to truncate the unphysical quadratic rise at large wave numbers. Assuming instantaneous transitions from inflation to the epochs of radiation and matter domination, we had shown that the regularized PS oscillates with a constant amplitude about a vanishing mean over small scales during these epochs. We had also smoothed the transition (actually, the `effective potential' governing the equation of motion of GWs) from inflation to radiation domination using a linear function and evaluated the regularized PS of PGWs post inflation. In such a case, we had shown that, over small scales, while the regularized PS continues to oscillate about zero, its amplitude decreases as $k^{-1}$. In this work, using the Born approximation, we examine the behavior of the regularized PS of PGWs over small scales when they are evolved through smoother and smoother transitions from inflation to the epochs of radiation and matter domination. We illustrate that, at small scales or high frequencies, the suppression in the regularized PS of PGWs occurs more and more sharply as the transition is smoothed further and further. With the help of examples, we also show that, in the case of transitions described by infinitely differentiable `effective potentials', the regularized PS of PGWs exhibits an exponential suppression on small scales. We argue that the observation of the exponential drop in the PS of PGWs can help us determine the energy scale and the time of the end of inflation. We clarify related issues and discuss the wider implications.

Non-Hermitian lattices can host the non-Hermitian skin effect, a boundary-induced collapse of all bulk eigenstates into exponentially localized edge modes. This effect underlies anomalous bulk-boundary correspondence and remarkable enhancements in non-Hermitian sensing, yet direct energy-resolved access to the eigenmodes of non-Hermitian lattices has remained limited. Here we report band- and energy-resolved eigenmode spectroscopy of skin modes in a frequency synthetic dimension. By introducing strong frequency-domain boundaries in an electro-optically modulated ring resonator, we realize finite non-Hermitian lattices and use laser detuning as a spectroscopic axis for the eigenenergies of the effective Hamiltonian. Site-resolved heterodyne measurements then reconstruct the spatial profile of each mode, revealing boundary-localized skin states throughout the spectrum and their eigenenergy-dependent displacement from the edge. Beyond 1D, the same frequency-boundary architecture, upon incorporating long-range couplings between finite lattices, produces genuine 2D frequency lattices rather than the hitherto-realized folded 1D systems on twisted tubes. In these lattices we observe tunable directional transport and edge localization in two synthetic dimensions. Our results introduce eigenmode spectroscopy as a direct probe of non-Hermitian physics and establish strongly bounded frequency lattices as a flexible platform for Hamiltonian engineering.

We investigate the quasinormal modes of a massless scalar field on an effective rotating loop quantum black hole background, constructed from a covariant spherical model via an improved Newman-Janis algorithm. Using the continued fraction method, we compute the spectrum for both fundamental and overtone modes, and systematically analyze how the frequencies depend on the quantum correction, spin, and angular structure of the perturbation. For all fundamental modes, increasing the quantum gravity correction monotonically reduces both the oscillation frequency and the damping rate, signaling slower oscillations and prolonged decay. Rotation imprints a nontrivial modulation: for a spherically symmetric perturbation, the real frequency displays a crossover as the spin grows, whereas this feature is suppressed once angular momentum is turned on; further activating the azimuthal component enhances the frequency and reduces the damping even more strongly. In the overtone sector, the rotating solution retains the hallmark quantum gravitational signatures of the spherical case - overtone outbursts and non-monotonic evolution - with rotation shifting these phenomena to weaker quantum corrections. Nonzero orbital angular momentum suppresses the outbursts, while the azimuthal degree of freedom boosts the frequency, giving rise to novel spectral inversions among higher overtones. Our results confirm that the effective rotating metric captures essential loop quantum gravity features, providing clear theoretical benchmarks for black hole spectroscopy and future gravitational-wave observations.

We show that a charged fluid endowed with an internal spin degree of freedom naturally satisfies the Pauli equation for a nonrelativistic spin-1/2 particle, and that a collection of n such interacting fluids can be reformulated as an Euler flow in 3n dimensions, thereby providing a natural representation of a system of n Pauli particles. These results provide a fluid-mechanical derivation of the Pauli equation and extend the Madelung, or quantum-hydrodynamic, picture to many-particle quantum systems. In particular, they imply that an n-qubit quantum computer can, at least in principle, be realized as a suitable combination of n fluids, or equivalently as a 3n-dimensional Euler flow.

In this work, we show that a two-qubit entangled state admitting at least one pure steered state is Einstein-Podolsky-Rosen (EPR) steerable from Alice to Bob. Pure steered states signifies that the quantum steering ellipsoid of Bob is tangent to his Bloch sphere at least at a single point. Furthermore, we prove that for a two-qubit entangled state, Bob's quantum steering ellipsoid is tangent to his Bloch sphere at exactly $N$ points, for $N\in \{ 0,1,2,\infty\}$, if and only if Alice's quantum steering ellipsoid is tangent to her Bloch sphere at exactly $N$ points. For any two-qubit entangled state, therefore, if one party can steer the other to at least one pure state, the state is two-way EPR steerable. We also present several illuminating instances of two-qubit entangled states such that the EPR steering can be verified in terms of pure steered states. Our result addresses the Gisin theorem in a EPR steering scenario: at least a single pure steered state implies two-way steering.

Low-energy estimation and state preparation for general $k$-local Hamiltonians are fundamental challenges in quantum complexity theory. For constant relative accuracy, Buhrman et al. (PRL 2025) recently broke the natural Grover bound $O(2^{n/2})$, where $n$ denotes the number of qubits, for both problems. In this paper, for any sufficiently small parameter $d\ge 0$, we present an even faster quantum algorithm that outputs a quantum state with energy bounded by the minimum energy over all depth-$d$ states (i.e., states obtained by applying a depth-$d$ circuit to the all-zero state), together with an estimate of this energy. For the class of Hamiltonians with depth-$d$ ground states, our algorithm furthermore achieves exactly the same energy guarantees as Buhrman et al. Our results also provide insight into the distinction between strongly entangled states and those admitting efficient classical descriptions.

Counter-diabatic (CD) driving provides a powerful route to fast and robust state preparation by suppressing diabatic excitations during finite-time evolution. Yet, deriving analytical CD protocols for complex systems remains challenging, motivating the development of variational approaches. These methods typically rely on minimizing trace-based functionals to construct approximate control Hamiltonians. However, in unbounded systems, such functionals can become ill-defined because of the unbounded bosonic Hilbert space, leading to divergent cost functions and unphysical variational coefficients. Here, we introduce a variational optimization framework equipped with physically motivated renormalization schemes that regularize the trace-based metric by restricting it to relevant displaced and low-energy subspaces. As a paradigmatic example, we apply our method to the quantum Rabi model beyond the dispersive approximation and identify two distinct CD contributions that couple the atomic degree of freedom to the position and momentum quadratures of the field. These terms suppress diabatic excitations across coupling regimes ranging from strong to deep-strong light--matter interaction. We further formulate a fidelity-based quantum optimal-control strategy that bypasses the limitations of trace-based variational methods. Finally, we show that the resulting CD terms can be implemented via Floquet engineering through parametric modulation of the native Hamiltonian. Our results demonstrate that CD driving can be consistently extended to continuous-variable systems with unbounded Hilbert spaces, providing a controlled and scalable framework for quantum control in strongly interacting light-matter platforms.

Experimental constraints on the gravitational inverse-square law at short range are presented, employing a consistent formalism across a wide range of length scales. We provide comprehensive updates from the past decade, building upon our previous review. This work facilitates the direct comparison of experimental results with theoretical models that extend general relativity. Furthermore, a comparison between various model parametrizations, including extra-dimensional models, is introduced. Finally, results from tabletop experiments are compared with those from high-energy collider experiments for both Yukawa and power-law potentials.

Black hole physics currently lacks a fully coherent understanding of the black hole mass (density), entropy, and interior (non-)singularity. These concepts are related to the black hole radius, area (of the horizon), and volume (within the horizon), respectively, in the Schwarzschild solution to Einstein's field equations. In this work, we argue that these concepts can be given reasonable interpretations in terms of spacetime's thermodynamic degrees of freedom, which constitute the metric, when the black hole is considered as a condensate thereof. Recent observations of black hole merger events support our proposal.

We study spin-lattice relaxation times of electron spins in Er$^{3+}$:CaWO$_4$ at millikelvin temperature, detected via their coupling to a low-mode volume superconducting resonator. At large magnetic field supporting strong phonon-emission rates, we observe a noticeable increase in relaxation times with increasing spin-excitations, which exhibit a unique $[\tanh (\hbar ω_0/k_\text{B} T)]^2$ temperature dependence. These observations are typical of a phonon-bottlenecked spin relaxation, and have implications for quantum technologies that exploit rare-earth spin ensembles as coherent resources.

In the phenomenological study of exotic hadrons, the sign of the effective range, $r_0$, is invoked as a criterion to distinguish between compact multiquark configurations (associated with $r_0 < 0$) and loosely bound hadronic molecules ($r_0 > 0$). Motivated by this, we investigate the fundamental constraints on the sign of the effective range for single-channel local interactions. We rigorously prove that for finite-range potentials, characterized by an inner repulsive core and an outer attractive tail, the effective range remains strictly positive provided that the scattering length is greater than the range of the potential ($a > R$).

The study of shadows of static and rotating black holes immersed in various dark matter halo profiles has gained significant attention in recent years. In this paper, we consider the static black hole solution immersed in three dark matter halo profiles (King, Hernquist, and Moore) and, by using the Newman-Janis algorithm, obtain the corresponding rotating black hole solutions. The main goal of the paper is to study the influence of the characteristic density, characteristic radius, and the spin parameter on the inner and outer horizons, the ergoregion, and the shadow radius, and to compare the results with Kerr black holes and other dark matter profiles. Our findings indicate that the shadow radius of rotating black holes immersed in dark matter increases compared to that of the Kerr black hole in the absence of dark matter; however, the impact on the size and shape of the shadow is negligible for King, Hernquist, and Moore profiles, and for all other dark matter halo profiles considered, and is independent of whether the halo is cuspy or cored. Therefore, at least for these Kerr-like black holes, the black hole shadow is not a suitable tool for distinguishing the nature of the dark matter distribution in galactic centers.

A common approach in physics and mathematics is to extend and modify theories and frameworks by considering what is often described as a `natural' extension or modification by including higher-order terms or by introducing other non-linearities. We show that such an approach must be taken with care as physical models can be connected in indirect ways. What looks like a natural approach in one setting will likely not be natural in another. We use the flat Friedmann-Lemaitre-Robertson-Walker equations of cosmology to connect the generalized uncertainty principle to modified theories of gravity. A simple additional term in one setting leads to enormous complications in the other. We identify Born-Infeld models as the only ones which appear natural in both settings.

We examine a proposed population of primordial, electrically charged compact objects, which we call astrons, with fiducial parameters \(M_A\sim10^{12}M_\odot\), \(Q_A\sim4\times10^{32}\,\mathrm{C}\), and megaparsec-scale separations. We analyze charge generation, ordinary accretion saturation, charge persistence in an ionized medium, plasma screening, the Reissner--Nordström and Kerr--Newman geometric regimes, lensing, and the possible use of Lyman-\(α\) absorption as a probe of astron electric fields, and the cosmological interpretation of a sparse charged population. The large-charge branch is not obtained from ordinary accretion saturation; it should be treated as a primordial or early-universe charge-concentration hypothesis. A horizon-mass estimate places a \(10^{12}M_\odot\) primordial object at times of order months after the Big Bang, so any relation to the early structures observed by the James Webb Space Telescope would be indirect, through later baryonic assembly around dark seeds. The main constraints are severe: plasma screening and neutralization must be avoided, the fiducial charge drives the exterior into a super-extremal regime without a Reissner--Nordström photon sphere, and the homogeneous interaction energy of a charged population scales as \(a^{-4}\). Thus the simplest FLRW perfect-fluid reduction does not generate asymptotic late-time acceleration. Any viable cosmological role for astrons must instead come from a controlled inhomogeneous Einstein--Maxwell averaging problem beyond the homogeneous approximation.

Time delays are a key observable in strong gravitational lensing systems. Their theoretical expression is usually written as a sum of a geometrical delay and a Shapiro delay, with cosmology entering through angular diameter distances and a redshift prefactor. In this work we derive this structure from the exact null geodesics of the Schwarzschild-de Sitter metric. The standard formula is recovered as the leading term in a small-angle expansion, and we identify the first correction to the usual geometrical-plus-Shapiro split. Such correction does not introduce any new cosmological dependence: it corresponds instead to a higher-order correction intrinsic to the Schwarzschild part of the metric. As a consequence, up to this order, the cosmological constant enters only through the unlensed angular diameter distances and the unlensed lens-redshift prefactor.

Multi-controlled Toffoli gates are fundamental building blocks in quantum computation, with applications in quantum arithmetic, simulation, and search algorithms. In fault-tolerant architectures, their realization is constrained by the high cost of non-Clifford resources, particularly in terms of T-count and T-depth. Recent advances have demonstrated that the use of ancillary qubits, relative-phase Toffoli gates, and dynamic circuit techniques can substantially reduce this overhead. In this work, we investigate the decomposition of large Toffoli gates using 3- and 4-input relative-phase Toffoli gates in the presence of a single clean ancilla and conditionally clean ancillas. We derive explicit resource bounds for Clifford+T implementations incorporating dynamic-circuit-based uncomputation and measurement-conditioned corrections. Our analysis emphasizes T-depth reduction under fixed CX and T-count overhead, ensuring relevance for near-term devices. We show that introducing 4-input relative-phase Toffoli gates enables significant T-depth reductions through enhanced parallelism while maintaining favorable ancilla requirements. We further validate our theoretical results through experimental evaluation and comparative analysis with existing approaches.

The Toffoli gate is a fundamental building block for quantum arithmetic and reversible logic, yet its efficient realization remains a major challenge in both near-term and fault-tolerant quantum architectures. Recent advances in dynamic quantum circuit capabilities, including mid-circuit measurement and classical feedforward, provide new opportunities for reducing the resource overhead of non-Clifford operations. In this work, we propose a set of dynamic decomposition strategies for multi-controlled Toffoli gates that exploit adaptive circuit execution and ancilla-assisted constructions. Our methods systematically reduce entangling-gate count, T-count, and T-depth compared with conventional static decompositions, while preserving fault-tolerance guarantees. Through analytical cost models and experimental evaluation, we demonstrate that relative-phase primitives and measurement-conditioned corrections enable scalable implementations with improved depth and resource efficiency.

We study the ground-state density patterns of a population-imbalanced two-component dipolar Bose-Einstein condensate confined in a circular quasi-two-dimensional box. Using a mean-field model, we map out phase diagrams as functions of the axial confinement, interaction imbalance, and population ratio. The system supports a rich sequence of stationary morphologies, including a nearly uniform pancake state, pancake-droplet and ring-droplet coexistence states, droplet arrays, and concentric rings. These patterns show a close structural correspondence to microphase-separated morphologies in diblock-copolymer systems, with the population imbalance acting as an effective volume fraction that selects the pattern topology. Analysis of the density profiles and structure factors reveals that the modulated states possess an intrinsic nonzero characteristic wave vector, which remains essentially unchanged when the box size is varied. We also find that the characteristic pattern spacing scales linearly with the axial confinement length, indicating that the transverse thickness of the condensate controls the effective in-plane length scale. In a finite circular box, this smooth scaling is interrupted by discrete steps, reflecting geometric frustration and the integer locking of the number of rings or droplets. Our results show that box-trapped dipolar mixtures provide a controllable platform for studying finite-size pattern selection and nonlocal microphase formation in quantum fluids.

This work investigates the impact of seasonal variations in seismic noise on the low-frequency performance of the Einstein Telescope (ET) at the Sardinia candidate site, focusing on implications for compact binary coalescence observations. Using seismic data collected between 2022 and 2025 in deep boreholes, we characterize monthly noise variations and identify representative best and worst case scenarios, corresponding to July and December. The measured seismic spectra are used to estimate the Newtonian noise contribution in the 2-10 Hz band and to derive modified ET sensitivity curves. These are implemented in a simulation framework to evaluate their effect on the signal-to-noise ratio (SNR) of binary neutron star and intermediate mass black hole signals, assuming the triangular ET configuration. We find that the low seismic noise of the Sardinia site results in only minor seasonal variations in detector sensitivity. The corresponding impact on SNR is limited to a few percent, even without including Newtonian noise mitigation. These results indicate that seasonal environmental fluctuation have a minor effect on the early inspired detectability of compact binaries, confirming the suitability of the Sardinia site for achieving ET low-frequency sensitivity goals.

Integrated time-bin-entangled photon-pair source with cavity-enhanced nonlinear optical processes is essential for quantum information technologies. However, microcavities with a high quality factor inherently introduce a trade-off between generation efficiency and photon bandwidth, which hinders the development of high-speed quantum networks with an integrated source. Here, we address this challenge by optimizing the nonlinearity property of the material and the geometry of the integrated microring resonator with a 4H-silicon carbide platform. Operating at a loaded quality factor of 1.9 $\times$ 10^5 - spectral bandwidth of 1.0 GHz and pumped with 300-ps double pulses separated by 1.25 ns at a repetition rate of 160 MHz, the device achieves a time-bin-entangled photon-pair generation rate of 1.35 $\times$ 10^7 s^-1 mW^-2. A raw visibility of 95.55 $\pm$ 0.18% is measured, showing a violation of Bell's inequality by more than 138 standard deviations, and a fidelity of 94.37 $\pm$ 0.22% is obtained by quantum state tomography. These results provide a scalable pathway to an efficient and broadband time-bin entangled quantum light source, overcoming intrinsic limitations of cavity-based designs and advancing integrated platforms for future quantum communication networks.

Quantum contextuality provides a fundamental signature of nonclassical behavior that cannot be explained by noncontextual hidden-variable models. We propose and experimentally implement a linear-optical setup for demonstrating Kochen-Specker contextuality via a violation of the KCBS inequality using single photons. Our scheme employs sequential measurements realized with linear-optical networks and on-off photodetectors. The construction ensures that each co-measured observable is implemented by the same physical operation across different contexts. Our experimental results demonstrate a clear violation of the KCBS inequality and robustness against photon loss. Beyond fundamental investigations, the proposed setup provides a practical tool for probing non-classicality and photon-number statistics of quantum states, which in turn enables the verification of single-photon sources.

Classical microwave drives are usually treated as ideal phase-coherent work sources for superconducting-qubit control. What if such a drive is replaced by a finite quantum battery. As a demanding benchmark, we consider echo-refocused geometric Landau--Zener interferometry powered by a single quantized bosonic mode. The qubit--battery dynamics are described by a Jaynes--Cummings Hamiltonian, while the echo pulse is retained as a qubit-only refocusing operation that cancels the dynamical phase. In the macroscopic coherent-state limit, the usual classical geometric interferometer is recovered. At finite mean photon number, however, the Jaynes--Cummings coupling generates photon-number-resolved avoided crossings with gaps $Ω_n=2g\sqrt{n}$. The qubit-only echo redistributes amplitudes between neighboring excitation sectors, so the finite-battery protocol is not a single classical interferometer but a coherent sector-resolved quantum evolution. This produces contrast loss, interferogram distortions, and measurable battery back-action. We further show that reducing photon-number fluctuations alone is not sufficient: geometric control requires a first-order phase reference. Geometric Landau--Zener interferometry therefore provides a practical benchmark for certifying phase-coherent quantum-battery energy.

Two primary formation channels for black holes have been proposed: the astrophysical channel, driven by the collapse of massive stars, and the primordial channel, involving their direct formation from density fluctuations in the early Universe. The key distinction between astrophysical black holes (ABHs) and primordial black holes (PBHs) is that PBHs can form at very high redshifts, before any stars have formed, leading to different stochastic gravitational-wave backgrounds (SGWBs). These SGWBs arise from the superposition of unresolved gravitational-wave signals accumulated over all redshifts. In this work, we employ the Hierarchical Bayesian Inference (HBI) framework and the publicly available GWTC-4 data to infer the population hyperparameters of PBHs. We then compute the SGWBs from ABHs and PBHs separately, accounting for the lensing effect, which can modify the strain amplitude of the SGWBs. By comparing the resulting SGWBs with the power-law integrated (PI) sensitivity curves of ground-based gravitational-wave detectors -- LIGO and the Einstein Telescope (ET) -- we find that both detectors can distinguish between these two black hole formation models within specific frequency ranges. However, LIGO is limited to a single method for distinguishing these models, and the lensing effect alters the frequency range over which discrimination is possible. In contrast, ET is capable of distinguishing ABHs from PBHs across a broader parameter space.

Quantum work statistics differ from classical ones because initial energy coherence matters. The standard two-point measurement (TPM) gives a positive distribution but erases phase information. Coherence-retaining endpoint-work quasistatistics provide a compact probe of shortcut-to-adiabaticity performance. For work defined with respect to a reference Hamiltonian, an exact counterdiabatic shortcut pulls the final reference Hamiltonian back to an operator diagonal in the initial energy basis. Endpoint Kirkwood-Dirac or Margenau-Hill quasistatistics then lose sensitivity to initial coherence and reduce to the TPM result. Imperfect shortcuts restore this sensitivity: a non-commuting control error produces off-diagonal pulled-back Hamiltonian elements at first order in the error amplitude, whereas population-only transition probabilities change only at second order. Harmonic-oscillator and qubit benchmarks confirm this linear-versus-quadratic contrast. The result complements inclusive work-cost analyses: it does not measure the auxiliary field's energetic cost, but provides a phase-sensitive endpoint diagnostic of residual nonadiabaticity.

Transmission through potential barriers is a fundamental problem in quantum mechanics. While semiclassical methods can approximate certain aspects of transmission, they fail to capture the intrinsically quantum interference associated with Wigner-function negativity. We numerically study the transmission of displaced Fock states through an inverted-oscillator barrier, with and without a Kerr nonlinearity that offers a potential route to experimental realization. These states allow only an approximate classical description, since their characteristic Wigner-function negativity is absent in phase space. The semiclassical simulation reproduces the overall transmission but deviate from exact results and fail to capture short-time plateaus that arise when regions of Wigner-function negativity cross the barrier. With the Kerr nonlinearity, reflections from nonlinear boundaries drive interference into classically forbidden regions, an effect that is inaccessible to semiclassical approaches. We find that these interference effects do not alter the maximum transmission probability, which is bounded by the initial positive-energy fraction and therefore already encoded in the phase-space structure of the Fock states. Because Fock states cannot be faithfully represented within classical phase space, the transmission through a barrier reveals fundamental limitations of semiclassical approaches.

We present a novel parameterized 4-qubit Eisert-Wilkens-Lewenstein (EWL) quantum game circuit for recommender systems in quadruple helix innovation ecosystems (academia, industry, government, and civil society). The local strategy operators $U_{i} = R_y(θ_{i})$ for each helix actor are directly tuned by normalized dominance weights extracted from real participant funding data (\texit{ecContribution}) in the European Commission CORDIS Horizon Europe database (project COVend, ID 101045956). The circuit employs a multi-qubit EWL entangler followed by parameterized local rotations, inverse entangler, and full measurement, achieving only 22 gates and circuit depth 11 while scaling as $O(n)$ for $n$-round helix communications. Measurement probabilities after the quantum game serve as recommender scores for disruptive versus sustaining innovation trends. These scores are subsequently mapped into the diagonal Dirac potential of a Dirac-Solow-Swan Hamiltonian, enabling time-evolution simulation of capital accumulation and bifurcation dynamics under disruptive innovation. Numerical experiments on real CORDIS quadruple-helix collaboration networks demonstrate the circuit's NISQ compatibility and its ability to forecast disruptive capital trajectories with high fidelity. The proposed framework bridges quantum game theory, parameterized quantum circuits, and relativistic economic growth models, offering a computationally efficient tool for innovation policy and strategic decision-making in complex socio-economic ecosystems. Complexity analysis and reproducibility are provided through open Qiskit implementations.

We investigate slow-roll inflation within the framework of Kaniadakis and dual Kaniadakis cosmology, where the usual entropy formalism is generalized through a deformation parameter $κ$. By deriving the modified Friedmann equations and the corresponding inflationary dynamics induced by Kaniadakis entropy, we analyze the deviations from standard inflation arising from $κ$-corrections. We compute the scalar and tensor spectral indices, the tensor-to-scalar ratio, and examine the observational constraints on the deformation parameter. Our results show that consistency with current observational data imposes stringent bounds on the deformation parameter $κ$. In the standard Kaniadakis formulation, viable slow-roll inflationary scenarios compatible with the Planck constraints on the scalar spectral index $n_s$ and the tensor-to-scalar ratio $r$ can be obtained, although the allowed values of $κ$ are strongly suppressed. For the dual Kaniadakis formulation, we find that the primordial power spectrum of scalar perturbations can remain consistent with observational data within certain parameter regions. We also verify the compatibility of the predicted scalar power spectrum with the latest Planck results and discuss the phenomenological implications of the $κ$-induced corrections. These findings suggest that Kaniadakis cosmology may leave potentially observable imprints on primordial perturbations, providing a possible connection between non-extensive thermodynamics and the physics of the early universe.

The barren plateau phenomenon, in which cost-function gradients of variational quantum algorithms vanish exponentially, remains a central obstacle for near-term quantum computing. Existing analyses typically depend on t-design or Haar-random assumptions and bound quantities at the level of unitary distributions, offering limited insight for designing probability measures on the parameter space of parameterized quantum circuits. In this paper, we introduce the structural $f$-divergence, a symmetric $f$-divergence-based measure between probability distributions on the parameter space. We establish analytically trade-off inequalities that bound the discrepancies in the expected gradient magnitude and in the cost-function moments between a distribution on PQC and a reference distribution; equality is attained by a minimal one-qubit, one-layer ansatz. As applications, we derive necessary conditions on probability measures for avoiding BPs and cost concentration, and sufficient conditions that suppress noise-induced deviations.

We propose a quantum sidecar architecture family for future hybrid AI training and inference. The central idea is not to store an entire Transformer in a small quantum memory, nor to claim one-shot collapse into a fully trained model or an optimal answer. Instead, we identify two physically distinct operating modes for quantum co-processors attached to classical large-model pipelines. The first is a stateful protected-register mode, in which a protected register stores a reusable quantum resource while an ancilla or temporary register performs QND-style readout. The second is a stateless reset-and-reprepare mode, in which each query prepares a task-conditioned quantum circuit, evolves over bounded training or inference control variables, measures candidate signals, resets the qubits, and repeats. We simulate the stateful mode using 2/4/6/8 protected-qubit density-matrix QND-style parity readout with one ancilla and a Qiskit cross-check. For the stateless mode, we include both an abstract candidate-update sampler and a circuit-level QAOA-style statevector sampler over structured candidate landscapes, followed by reset-overhead sensitivity analysis. The resulting framework positions quantum sidecars as bounded signal generators for optimizer-side sampling, adapter or expert selection, retrieval, routing, and reasoning-path proposal. As a speculative outlook, we introduce quantum weight-state sidecars: restricted quantum representations over model-control variables, not direct encodings of complete classical weight tensors.

Backward diffusion is a prototype ill-posed evolution: high spatial frequencies grow exponentially in time, so mesh-based time marching without explicit regularization is quickly overwhelmed by noise. Schrödingerization embeds the semidiscrete generator into Hermitian dynamics on an extended space; we ask whether McLachlan projection onto a fixed low-dimensional frame supplies a structured regularizer whose error budget can be read from a projection defect that separates full lifted propagation from the reduced trajectory. We prove uniqueness of the reduced flow, Gram-norm conservation, a lifted--reduced gap bound in terms of that defect, and perturbation estimates that highlight overlap-matrix conditioning when matrix elements are estimated statistically. We also spell out a fair classical baseline -- spectral low-pass or Tikhonov filtering on the same semidiscrete model, with bandwidth or ridge strength matched to the information content of the chosen frame -- so numerical contrasts isolate the Schrödingerized reduced pipeline rather than an unregularized Crank--Nicolson march that mainly showcases blow-up. A calibrated one-dimensional benchmark pairs a spectrally truncated reference with snapshot-built subspace evolution and finite-shot Qiskit Aer estimation, illustrating how lift, projection, and sampling layers contribute differently to the overall error.

Recent progress in gravitational wave observations has positioned Pulsar Timing Arrays as a key tool for detecting the stochastic gravitational wave background in the nanohertz band. It is widely believed that this background is primarily attributed to the cosmic ensemble of inspiraling supermassive black hole binaries. While traditional analyses have predominantly focused on the spectral amplitude and frequency dependence of the gravitational wave background, higher-order statistics such as variance, skewness, and kurtosis could potentially be useful for extracting further physical information. However, these statistical moments are known to diverge when the redshift integration is extended down to z=0. In this study, we propose a strategy to resolve this issue by introducing a physically motivated lower integration limit, z_min, defined by the sensitivity for detecting individual sources. Since higher-order statistics are primarily determined by local sources, we may adopt the lowest-order approximation with respect to redshift in their computations. Under this approximation, we demonstrate that all higher-order statistics beyond the expectation value depend on the mass function only through a weighted average of the chirp mass, <\mathcal{M}^{10/3}>, irrespective of the redshift evolution model. We show that the ratio of the variance to the expectation value provides information on <\mathcal{M}^{10/3}>/<\mathcal{M}^{5/3}> independently of the total number of mergers. We also find a consistency relation between the kurtosis and the squared skewness, paving the way for testing the binary-origin hypothesis of the gravitational wave background. Our findings demonstrate that higher-order statistics provide a new window for interpreting the gravitational wave background, offering a methodology to break existing degeneracies and refine our understanding of the mass function.

We report the theoretical prediction and experimental observation of a new class of four-dimensional (4D) tensor singularities and their three-dimensional (3D) Euler-class descendants, protected by chiral and spacetime inversion symmetries on a superconducting circuit platform. The 4D point-like singularity/monopole, characterized by the Dixmier-Douady class of a real bundle gerbe associated with tensor gauge fields, is observed to evolve into a nodal ring carrying an additional first Euler class charge under symmetry-preserving perturbations. Dimensional reduction reveals 3D Euler and Euler curvature dipoles, exhibiting nontrivial Euler topology and a topological sum rule that ensures zero-energy flat bands inherit nontrivial topology even without interactions. Crucially, these high-dimensional degenerate systems are mapped and reconstructed using a hybrid analog-digital protocol designed for non-Abelian quantum geometry measurement within a superconducting qubit array. Our work not only expands the family of topological monopoles but also establishes a robust experimental framework for exploring high-order gauge theory and real-bundle topology across diverse quantum platforms.

Spontaneous parametric down-conversion (SPDC) is the most widely-used source of high-dimensional entangled two-photon states, and the entanglement in the spatial degree of freedom is considered best suited for harnessing high-dimensional advantages. Although the Schmidt basis provides a natural choice for state characterisation of entangled two-photon states in any degree of freedom, there is currently no technique that can measure the Schmidt basis of an entangled two-photon field. The existing techniques can only reconstruct the Schmidt spectrum when the Schmidt basis is known a priori. In contrast, we present a technique that measures the complete set of spatial Schmidt modes without any prior knowledge. Using this technique, we report measurement of states with over 3000 Schmidt modes -- highest reported yet -- with up to 98$\%$ fidelity. We expect our work to significantly advance the harnessing of high-dimensional advantages in SPDC-based systems.

Anderson localization is fundamentally controlled by dimensionality, yet the nature of the Anderson transition in continuously tunable noninteger dimensions remains largely unexplored. Here, we introduce a family of three-dimensional fractal lattices with continuously tunable spectral dimension $d_s\in[2,3]$, providing a controlled platform for studying localization physics beyond integer dimensions and across the lower critical dimension $d_s=2$. Using large-scale finite-size scaling analysis, we systematically investigate the Anderson transition and identify mobility edges throughout the fractal family. The critical disorder strength evolves continuously from $0$ to $16.6$ as the spectral dimension increases from $2$ to $3$. We show that the spectral dimension predominantly governs the universality class of the transition, while the precise critical point is additionally influenced by microscopic geometric details of the underlying fractal lattice. The critical exponent exhibits an approximate inverse dependence on $d_s$, providing quantitative insight into scaling theory in noninteger dimensions. Our results establish tunable fractal lattices as a versatile framework for exploring localization and quantum critical phenomena beyond conventional integer-dimensional systems.

Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately $5\%$ in execution time, $30\%$ in communication overhead, $40\%$ in wait time and $2\%$ in fidelity, providing better solutions to efficient allocation strategies.

We show that the emergence of time evolution in an otherwise timeless nonrelativistic closed quantum system -- viewed as a poor man's model of generally covariant quantum theory -- can be understood from the perspective of the path integral representation. As often happens in the functional integral approach, this viewpoint offers a more intuitive account of features that become cumbersome in the operator/Hilbert-space formulation. We show how Schrödinger evolution emerges once a clock degree of freedom is identified and placed in a suitable semiclassical `good-clock state'. Our analysis has a consequence that extends to path integral formulations of generally covariant systems with action $S$ (including gravity). In such theories certain transition amplitudes take the form $\exp(iS/\hbar)+\exp(-iS/\hbar)$ rather than the expected `forward propagating' $\exp(iS/\hbar)$. This feature, known as the {\em cosine problem}, appears in concrete regularizations of the path integral, for example in the spin foam representation defining the physical inner product between spin network states in loop quantum gravity. Both formally and in explicit regularizations, this apparent difficulty has led some authors to seek modifications of the basic amplitudes to eliminate backward propagation. Our model shows that the cosine problem is instead a natural consequence of time-reversal invariance of the fundamental dynamics together with the time-neutral boundary states commonly used in transition amplitudes. When a suitable clock system is identified and placed in a semiclassical `good-clock state', it introduces a time arrow selecting the `forward propagating' $\exp(iS/\hbar)$, without modifying the fundamental dynamics. The analysis clarifies how time emerges under suitable conditions and emphasizes that, in the canonical formulation, quantum gravity is fundamentally timeless.

Single-channel resonances are fundamental processes in scattering of atoms, yet their occurrence is largely incidental and lacks systematic control. In this Letter, we propose a mechanism to realize a continuously tunable single-channel resonance by controlling the temperature of the heteronuclear mixture. By extending the Casimir-like mediated interaction to finite temperature, we demonstrate that thermal smearing of the Fermi surface reshapes the effective potential between impurities, giving rise to a temperature-controlled resonance (TCR) over a wide parameter range. As a direct consequence, the resonance position shifts systematically with temperature variation, providing a clear experimental signature of this mechanism. We further investigate the quench dynamics of a Bose gas immersed in a Fermi sea and demonstrate that the observed temperature-dependent loss features in recent experiments are consistent with the TCR mechanism. Our results establish temperature as a simple and experimentally accessible control knob for single-channel resonances in ultracold quantum gases.

We identify a superspace mechanism behind equivariant localization in supergravity. We show that closed superforms generate, on supersymmetric backgrounds, equivariantly closed polyforms. After presenting the general mechanism, we construct such polyforms for vector and linear multiplets, and for chiral and BF action principles, in off-shell $4d$ $\mathcal{N}=2$ conformal supergravity, reproducing and extending recent results. Our construction provides a geometric first step toward equivariant localization of BPS observables in supergravity, including higher-derivative theories, and holography.

The quantum Mpemba effect concerns with anomalous relaxation of quantum states that evolves either under unitary or non-unitary dynamics. In the context of open quantum systems, while most studies focus on quantum states evolving under completely positive trace-presing dynamics described by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation, we demonstrate that an analogous effect can arise at the level of operators. In particular, we show that operators that evolves under the adjoint Liouvillian -- despite not being a trace-preserving map -- can still exhibit a genuine Mpemba effect. We derive general conditions under which this phenomenon can occur and validate our predictions for three different open quantum setups. Our results broaden the scope of the Mpemba effect in quantum systems and provide a framework for controlling the relaxation of physically relevant observables.

Quantum probability provides a novel framework for formulating machine-learning (ML) problems in Hilbert space. We introduce a prototype-based learning scheme where class representatives are encoded as generative matrix product states (MPS). Because these prototypes reside in the same Hilbert space as quantum-encoded data samples, various ML tasks such as classification and clustering can be performed through geometric measures of quantum states. This approach lifts prototype learning from classical feature space to quantum Hilbert space. Benchmarks on Fashion-MNIST and a real-world electrocardiogram dataset demonstrate that our method outperforms classical prototype approaches while remaining competitive with standard black-box neural networks. We also identify an ``attraction'' effect induced by the quantum-probabilistic prototypes and introduce a dimensionality-reduction scheme based on prototype distances. Our results establish quantum states as an explainable framework for prototype learning, opening new directions for designing ML algorithms in quantum Hilbert space.

Giant atoms, which couple to a waveguide through multiple spatially separated connection points beyond the dipole approximation, provide a versatile route for quantum information processing based on interference-induced bound states in the continuum (BICs). While multi-giant-atom architectures are being developed toward giant-atom quantum networks, the role of direct coupling between the giant atoms, in particular the associated coupling phase, in atomic dynamics remains insufficiently understood. Here we take a first step toward addressing this issue by studying a two-giant-atom waveguide-QED model. We show that the coupling phase can be used to control both the number of BICs and their profiles for both of photon and atoms. More interestingly, the presence of BICs gives rise to a variety of dynamical behaviors, providing an effective mechanism for tailoring quantum-state evolution in giant-atom waveguide-QED systems. Our results highlight coupling-phase engineering as a useful tool for controlling interference, bound states, and quantum dynamics in nonlocal light--matter interfaces.

This work presents a new class of exact analytic rotating black hole solutions within five-dimensional generalized Proca theories. Through a Kerr-Schild ansatz where the Proca field is set along a null geodesic congruence, the non-linear field equations reduce to a consistent set of three master equations. This geometric configuration ensures that the vector field remains light-like on-shell, effectively restricting the theory's functional couplings to discrete constants and allowing for a fully analytic treatment. The resulting solutions, incorporating a cosmological constant and two independent angular momenta, exhibit primary hair given by an arbitrary function of the non-Killing angular coordinate. We identify several solution branches defined by specific algebraic relations between the Proca coupling constants, providing a significant generalization of the Myers-Perry family. Notably, the metric retains a Kerr-Schild form identical to the Myers-Perry representation, with an additional contribution constructed from the tensor product of the Proca one-form with itself.

We complete the visible, hidden, sectorial, and discrete symmetries of Ernst-like potential spaces in stationary, axisymmetric Einstein-Maxwell-Scalar Field (EMSF) and Einstein-ModMax-Scalar Field (EMMSF) theories. In the real potential space \((f,ε,ψ,χ,κ)\), we determine the exact visible symmetries and their solvable Lie algebra. We characterize the hidden symmetries on invariant subspaces: Ehlers acts in the gravito-rotational sector, while electric and magnetic Harrison transformations act in static electromagnetic sectors. In the frozen EMMSF regime, \(v=v_0,\ w=w_0\), we show how EMSF sectorial transformations are deformed in ModMax theory. We also show that coexistence of electric and magnetic sectorial Harrison transformations imposes \(d w=0\) and \(d[(v^2+w^2)/w]=0\), selecting precisely the frozen ModMax sector. We study the Hamiltonian formulation, Noether charges, and Casimir invariants of the sectorial algebras. In harmonic branches of the \(A,B,C\) one-forms, the affine geodesic energy is constant, so the quadrature for \(k\) is controlled by the affine-geodesic Hamiltonian. The functions \(ω\) and \(A_\varphi\) follow from Noether charges along the Killing directions of \(ε\) and \(χ\), and are written using dual harmonic functions. We examine the electric and magnetic Lewis-Weyl-Papapetrou frames and their discrete map, which sends \(κ\mapstoκ^{-1}\), \(ψ\mapstoχ\), \(χ\mapsto-ψ\), and \(ε\mapstoε-ψχ\). Finally, we apply the sectorial transformations to harmonic scalar--acuum Weyl seeds with independent gravitational and scalar harmonics. Frozen-ModMax Harrison maps generate charged branches, while Ehlers generates the gravito-rotational branch. For these solutions we give the final quadratures for \(k\), \(ω\), and \(A_\varphi\).

Kerr quasinormal modes near algebraically special frequencies exhibit anomalous behavior, including apparent bifurcation, disappearance, and a nonsmooth connection to the Schwarzschild limit, which has remained puzzling for decades. Tracking poles and zeros of Green-function building blocks across different Riemann sheets, we show that the bifurcation is due to an avoided crossing accompanied by resonant excitation, while the disappearance is due to pole skipping caused by cancellation of a quasinormal-mode pole by a Matsubara-mode zero. This resolves the physical origin of these long-standing anomalies.

Ultrafast dynamics of collective excitations in superfluids remains largely unexplored beyond the roton regime, despite its importance for understanding nonequilibrium processes in these systems. Here, we employ ultrafast coherent control with sequences of femtosecond pulses to perform spectroscopy of multiple branches of the Landau excitation spectrum in superfluid helium far from equilibrium. By measuring the time-resolved optical birefringence, we track the nonequilibrium dynamics of maxon pairs and Pitaevskii plateau excitations alongside the previously studied roton pairs, revealing surprisingly strong binding energy of maxon pairs, their extremely short lifetime, and the influence of the quasiparticle effective mass on the phase of the coherent response. These results demonstrate the ability to extract previously inaccessible information about collective excitations in a strongly interacting quantum fluid by probing its nonequilibrium dynamics on femtosecond and picosecond timescales.

Trapped-ion quantum computers based on the quantum charge-coupled device architecture require on the order of ten trap electrodes per qubit, making the number of vacuum feedthroughs a bottleneck at the system scale. Time-division multiplexed (TDM)-based voltage control for trap electrodes provides a natural route to alleviate this constraint. However, previous studies have been limited to architectural proposals for static trap-potential compensation and room-temperature demonstrations of dynamic-electrode control, leaving cryogenic operation of TDM-based voltage control for static and dynamic electrodes experimentally unexplored. In this study, we develop and cryogenically validate TDM-based voltage control schemes for two distinct electrode classes. For static electrodes used in trap-potential compensation, we implement a 32-channel demultiplexed system operating at approximately 27~K, achieving an effective voltage update rate of 37.5~kHz with an output range of $\pm10~\mathrm{V}$ per channel. For dynamic electrodes used in ion operations, such as shuttling, we implement a four-channel demultiplexed system operating at approximately 14~K, achieving an effective voltage update rate of 1~MHz with a comparable output range. These results establish TDM-based voltage control as a practical approach for both electrode classes, providing a path for mitigating the vacuum feedthrough bottleneck in scalable trapped-ion quantum processors.

A Lagrange multiplier field restricts the quantum corrections to the Einstein-Hilbert action at one-loop order, yielding a model that is renormalizable and unitary while reproducing the Einstein field equations in the classical limit.

In this work we address the possibility of having a positive running of the spectral index in inflationary theories. The recent ACT data indicate mildly that the running of the spectral index might be positive, and several other physical indications point out this possibility. If the running of the spectral index is confirmed to be positive by future cosmic microwave background experiments, this can rule out quite popular inflationary scenarios. We investigate how it is possible to obtain a positive running of the spectral index in the context of minimally coupled scalar field gravity and modified gravity. For the modified gravity we choose two mainstream and of string origin candidate theories, $F(R)$ gravity and Einstein-Gauss-Bonnet gravity. In the case of scalar field inflation and $F(R)$ gravity inflation, we demonstrate the difficulties for obtaining a positive running of the spectral index for a viable inflationary regime, so scalar theories and $F(R)$ gravity are mostly compatible with the Planck data. But nuanced scalar field scenarios can be compatible with the ACT data and produce a positive running of the spectral index. In the context of Einstein-Gauss-Bonnet theories which are compatible with the GW170817 event, the running of the spectral index can easily be positive while in parallel having a viable inflationary era.

We propose a heralded protocol for generating multipartite free-electron entanglement from atomic $W_N$ resources in a sideband-resolved interaction regime. The scheme consists of $N$ independent electron--atom interaction arms, where each free electron couples locally to one two-level system. For uniform couplings and common detuning, the dynamics is solved analytically within the rotating-wave approximation. Projecting the atoms onto the all-ground state maps the initial atomic excitation manifold onto the electronic upper-sideband manifold and prepares an exact $N$-electron $W_N$-type state. The heralding probability is obtained in closed form for resonant and detuned regimes. At resonance, the optimal success probability obeys the large-$N$ scaling $P_{G_N}^{\max}\sim e^{-1}/N$. The heralded state retains the multipartite entanglement structure of the atomic resource, as shown for arbitrary $N$ and illustrated explicitly for $N=3$. Detuning, weak symmetry breaking, beyond-rotating-wave corrections, and Gaussian coupling envelopes are discussed. The protocol provides a scalable route toward multipartite free-electron entanglement generation from localized atomic resources within quantum electron optics.

The continuous variable quantum state generated by time-domain multiplexed optical parametric amplifier (OPA) is attractive because of the potential of enlarging the mode scale. Currently, the duration of temporal mode is longer than 100 ns since the OPA is pumped by the continuous wave laser, which restricts the scale of quantum state. Here we demonstrate multi-mode intensity squeezing localized in a train of short pulses with duration of $\sim10$ ps by using an unbalanced SU(1,1) interferometer (USUI), where the mode-locked laser is exploited as the pump and the time-domain multiplexing is realized by the combination of optical delay and nonlinear beam splitter. Using the pulse resolved joint measurements, we reveal the correlation structure of the state is unique and fundamentally different from previous approaches. Due to the globe quantum correlation, the intensity squeezing not only depends on the gain of OPAs but also ties to the mode number $M$ of joint measurement. We experimentally perform joint measurement among 30 modes and show the intensity noise lower than shot noise level by $\sim0.9$ dB is achievable for $M>10$. Our investigations open the door for generating ultra-large scale quantum state by pulse pumped USUI.

We propose granularity noise thermometry (GNT), a fluctuation-based optical thermometry scheme that exploits the intrinsic fluctuations of susceptibility arising from atomic discreteness. The power spectral density of transmitted light exhibits an excess noise above the shot-noise limit that scales linearly with the photon-to-atom ratio $\mathcal{R}$. Consequently, varying the incident power (hence $\mathcal{R}$) yields the slope $\mathcal{K}$ of this linear scaling, which directly encodes the temperature. Closed-form expressions for the polarizability moments are derived via the plasma dispersion function, which yield distinct temperature scalings: $\mathcal{K}\propto P_{\mathrm{v}}(T)/T^2$ for thermal vapors and $\mathcal{K}\propto T^{2}$ for cold atoms. While practical implementation requires careful control of technical noise and system parameters, the present framework provides a noise-based pathway for optical thermometry using atomic ensembles.

Quantum Tanner codes are a recently developed family of quantum error-correcting codes characterized by favorable asymptotic performance characteristics. Despite their theoretical potential, practical decoding algorithms that effectively leverage their structural properties remain limited. This work introduces LEAD (Local Ensemble-Assisted Decoder), a structure-aware decoding framework tailored for quantum Tanner codes. The proposed scheme leverages the decomposable structure of Cayley complexes to project the global code onto overlapping local subcodes defined by vertex neighborhoods, where error probabilities are estimated in parallel. To ensure global consistency, LEAD utilizes the inherent topological symmetry of the complex and introduces a soft-information regularization mechanism to mitigate local overconfidence during information aggregation. This framework enables highly parallelized, low-complexity decoding that is intrinsically compatible with various local search heuristics. Simulation results demonstrate that LEAD achieves significantly lower logical error rates than standard decoding framework while substantially reducing the average decoding latency and iteration count.

The extraction of weak signals plays a crucial role in quantum precision measurement, where the estimation results are often limited by low signal-to-noise ratios. Here, we demonstrate a parameter-estimation framework based on the adaptive extended Kalman filter for dynamic magnetic-field estimation in quantum systems using spin-noise measurements -- a challenging regime characterized by weak signals. By modeling the magnetic field as an unknown parameter, the proposed approach alleviates model dependence in state estimation. Furthermore, by introducing an adaptive algorithm with real-time noise estimation, our method overcomes the measurement noise intensity constraints of conventional extended Kalman filtering and enhances its practical applicability. Numerical simulations covering three representative magnetic-field dynamics validate the capability of the proposed framework, while experimental results demonstrate successful tracking of a seismo-magnetic-like signal beyond the intrinsic sensitivity of conventional spin-noise spectroscopy.

As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments. We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost. Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.

Building upon our prior work [1], we present a unified stochastic drift model (SdM) for superconducting charge qubits based on memory multi-fractional Brownian motion (mmFBM). The classical sector employs a time-dependent Hurst exponent H(t) and adaptive memory kernel K(t,s), capturing non-stationary 1/f^beta noise and long-range temporal correlations inaccessible to conventional models. The quantum extension is formulated via a time-dependent Caldeira--Leggett environment with spectral density J(omega;t) = eta(t) omega_c^{1-s(t)} omega^{s(t)} exp(-omega/omega_c), where s(t) = 2H(t)-1, consistently reproducing beta(t) = 2H(t)-1. Four central results emerge: (1) relaxation and noise amplitudes act independently on energy decay; (2) time-varying H(t) matches experimental 1/f spectra more accurately than any constant exponent; (3) adaptive kernel dynamics preserve correlations without artificial damping; and (4) simulations predict coherence times (T1 ~ 5.00 x 10^6 ns, T2 ~ 4.18 x 10^5 ns) consistent with theory when charge noise dominates. The qubit exhibits stretched-exponential Ramsey and echo decay, non-Markovian dephasing, and a temperature-driven quantum-to-classical crossover. We derive the effective time-local Lindblad master equation, establish the classical mmFBM limit at high temperatures, and provide experimentally testable scaling relations. The non-exponential decay patterns reveal fundamental limitations of Markovian approaches, and the framework guides the design of noise-resilient qubit architectures.

The electronic distribution of a molecular domain is examined in this study. A theoretical formulation of quantum molecular properties is presented using the Quantum Expectation Identity theorem (QEI), with a focus on the three-state model of the density matrix for the quantum state of a molecular domain as an open system. The report examines the relationship between ab initio statistical fluctuation-correlation theorems for quantum observables and their derivatives. We focus on three main quantities of a domain: the electronic population, its chemical potential, and its maximum capacity for accepting or donating charge with the neighbors. The analytical expressions for the quantities are presented and discussed in detail. At the end, we explore the concept of quantum purity and its proper application in the molecular domain.

Quantum batteries utilize nonclassical resources to achieve charging speed and energy storage performances that surpass classical thermodynamic limits. However, the practical realization of quantum batteries is often constrained by the inevitable environment-induced dissipation of both stored ergotropy and coherence. To actively counteract these losses, we propose a dual-channel coherence framework that exploits dark-state protection to stabilize ergotropy. We conduct, for the first time, an investigation of the synergistic interplay between internal charger coherence and reservoir squeezing, the latter acting as a source of external coherence. In the resource-efficient regime where charger and battery sizes are comparable, our study shows that internal charger coherence and reservoir squeezing jointly enhance the transient charging power. Crucially, initial charger coherence is the fundamental resource for maximizing and stabilizing steady-state ergotropy through dark-state protection. Our analysis reveals that these advantages are driven by the buildup of local battery coherence, which emerges from the integration of both internal and external coherence sources. These results offer a robust pathway for high-power, stabilized energy storage in quantum architectures.

In this work, we investigate the scattering and absorption of spin 0 particles for electrically charged black holes in two gravity models with spontaneous Lorentz symmetry breaking. The first one is the so-called bumblebee model that involves a vector field with a nonvanishing vacuum expectation value (VEV), while the second one involves a self-interacting Kalb-Ramond field coupled to gravity. For our purpose, we employ the partial waves method to compute the scattering cross-section and the absorption for these charged black holes. Moreover, we calculate the greybody factors (GFs) for spin 0 particles, showing the influence of both the LV parameter and electric charge.

We introduce a quantum Viterbi decoding algorithm for hidden quantum Markov models (HQMMs) motivated by quantum information processing and quantum algorithms. Given a finite sequence of measurement outcomes, the algorithm identifies hidden quantum trajectories that maximize a joint decoding functional, serving as a genuine quantum analogue of the classical Viterbi score. Unlike classical hidden Markov models, where decoding optimizes over a finite discrete state space, our method performs optimization over a continuous manifold of pure quantum effects, thereby exploiting coherent superpositions in the hidden memory. We prove a strict quantum advantage: coherent hidden trajectories can achieve decoding scores that strictly exceed any classical strategy constrained to diagonal (commuting) effects, even when both models share the same observed statistics. These results position quantum Viterbi decoding as a concrete quantum algorithmic primitive for sequential decision-making, with direct applications to quantum memories, quantum communication with memory, and near-term quantum machine learning on NISQ devices.

Direct quantum-annealer portfolio optimization is commonly formulated as a penalty-encoded QUBO and submitted to D-Wave hardware. We show that this standard formulation fails on current devices and identify the structural reason: the cardinality penalty contributes a dense rank-one term proportional to the all-ones matrix that makes the logical interaction graph complete regardless of the covariance structure. On Pegasus and Zephyr, chain-break fractions reach 83 percent at N equal to 24 and 92 percent at N equal to 49, producing no feasible samples. Attempting to fix this through topology-aware sparsification reveals a second problem: any sparsifier that removes off-diagonal entries also dilutes the cardinality constraint, so raw samples remain infeasible even when chains no longer break, and an ablation shows that for structurally favorable cases such as betting with settlement-graph priors the classical feasibility projector alone explains the result rather than the QPU. We propose dropping the penalty entirely: build an objective-only QUBO from the expected returns and the risk-scaled covariance, sample it on hardware, and enforce the cardinality constraint classically as a post-processing step. On D-Wave Advantage and Advantage2 for equities up to N equal to 49 and betting up to N equal to 48, mean chain-break fractions per sample averaged over reads drop from the range of 71 to 92 percent down to at most 0.04 percent. The QPU returns lower-energy feasible portfolios than the greedy heuristic on betting at N equal to 39 and 48, which is an energy comparison and not a proof of optimality, and the equity post-processed regret is at most 0.03 percent at all tested scales. These results establish that the penalty encoding, not the sparse hardware topology, is the binding constraint for direct QPU portfolio optimization at currently accessible scales.

We audit how much of D-Wave's hybrid quantum-classical portfolio-optimization service is actually quantum. On cardinality-constrained mean-variance-turnover instances spanning N equal to 10 to 640 with a Gurobi MIQP optimality anchor, the constraint-native LeapHybridCQM service matches Gurobi's proven optimum on all 54 instances where Gurobi proves optimality, but the mean QPU access time is only 0.034 seconds out of a 5-second wall-clock budget, roughly 0.7 percent of the run. The remaining roughly 99 percent is the service's classical decomposition, sub-problem assembly, and feasibility-aware reassembly, so the reported D-Wave hybrid win on this problem class is a constraint-native classical pipeline with a small QPU contribution rather than a quantum-sampling win. Two structural results sharpen this audit. First, the cardinality penalty contributes a dense rank-one term that makes the penalty-encoded logical graph fully connected regardless of the original covariance density, collapsing the intended density benchmark axis for all penalty-encoded paths while leaving the constraint-native sparsity intact. Second, the constraint-native service returns identical solutions at every tested wall-clock budget from 5 to 300 seconds and across 10 repeated calls, a determinism property of the service on this problem class. Together with two classical baselines, namely Gurobi MIQP and simulated annealing, and a comparison against the penalty-encoded hybrid interface, these results extend the prior constraint-native versus penalty-encoded observation of Sakuler et al. from the statement that the constraint-native interface handles constraints natively to the operational decomposition of where the win actually originates, a finding that reframes how D-Wave hybrid performance should be reported in quantum-finance benchmarks.

We formulate the junction conditions for Scalar-Tensor-Vector Gravity (STVG/MOG), proposed by J.~W.~Moffat. Using these conditions, the theory of gravitational collapse is constructed. In the collapsing process, an interior Friedmann-Lemaître-Robertson-Walker (FLRW) spacetime with baryonic matter and dark energy is matched with an exterior static, spherically symmetric Reissner--Nordström (RN)-like spacetime through a shell that carries STVG-charge. Starting from the standard STVG action, we derive the junction conditions across a boundary that relate the values of the various field quantities and their derivatives across the matching surface. Using the matching conditions and the nature of the collapsing shell, it is shown that a gravitational collapse can proceed in the present situation, and one can have RN-like horizon formation in finite proper time. We present two simplified models of gravitational collapse in this article: one ends up as an extremal RN-like black hole, and the other tends to collapse towards a sub-extremal RN-like black hole, as observed by an asymptotic observer at an infinite distance away from the collapsing system.

On a mixed-frustration 12-qubit Ising instance (seed 14029) run on two D-Wave generations, Advantage2 Zephyr and Advantage_system6.4 Pegasus, the late-time subsystem autocorrelation under cycled reverse annealing sits strictly between two equilibrium reference processes at the device-calibrated effective temperature: localized parallel tempering, and delocalized equilibrated path-integral simulated quantum annealing at a fixed Advantage2 pause-point transverse-field scale. The bracket holds on all three tested schedules and at both hardware calibrations. We obtain this result through two ingredients: a cycled reverse-anneal protocol (reinitialize_state=False, 50 cycles per submission) used as a Markov-chain probe of the device's pause-point dynamics, and a parallel-tempering falsification framework with bias-corrected and accelerated bootstrap 95% confidence intervals. Of eighteen tested (seed, schedule) combinations on Advantage2, three are PT-unmatched and correspond to two distinct Ising instances; an independent native-graph control with no minor embedding on a third mixed-frustration instance reproduces the same direction of mismatch. Among twenty random training instances, schedule shape modulates basin occupation on six of the thirteen multi-basin-in-readout instances, with dominant-configuration shifts of up to 38 percentage points including changes of the dominant configuration. A pre-registered linear predictor of schedule sensitivity from exhaustively computable landscape features fails on ten held-out instances, indicating that schedule sensitivity is not captured by simple linear functions of the tested landscape moments. The bracketing result revises an earlier two-pause-enhancement claim and reframes reverse-anneal schedules as instance-specific basin-occupation probes rather than universal quantum-enhancement knobs.

This note contains a simple construction of complete sets of MUBs, using bent functions to write the new basis vectors as explicit linear combinations of the standard basis.

Ordered realizations of discrete POVMs are studied through a residual transform generated by sequential tests. One application of the transform replaces each coordinate by the effect obtained after all earlier tests have failed, and appends the remaining mass as a terminal outcome. Under natural hypotheses, iterating the transform produces a collapsed POVM whose non-escape coordinates are the parts of the original effects that survive all earlier tests. The resulting collapse map gives an equivalence relation on ordered POVM realizations. Its range and fibers are characterized. The range consists of collapsed POVMs, whose non-escape coordinates are mutually orthogonal and whose support projections strongly sum to the identity. The fiber over a collapsed POVM consists of all ordered realizations with the same residually visible compressions. In particular, different ordered realizations, including ones with different off-diagonal coupling data, can have the same collapsed image. After collapse, the non-escape coordinates are fixed under further residual iteration. The remaining dynamics takes place in the escape effect, which is fragmented by a universal scalar functional calculus.

Ultralight bosons with self-interactions, such as axion-like particles, can form astrophysical Bose-Einstein condensates around stars or compact objects, often referred to as gravitational atoms. In this work, we adopt a recently proposed dynamical formation mechanism for these halos and estimate their impact on extreme- and intermediate-mass-ratio inspirals when present around the primary black hole. We show that, for signal-to-noise ratios $\lesssim 100$, LISA can distinguish gravitational waveforms from binaries embedded in such halo overdensities. Our analysis indicates that LISA can probe boson masses $m_\mathrm{dm}\sim10^{-17}$-$10^{-15}\,\mathrm{eV}$ and decay constants $f_a\sim10^{10}$-$3.2 \times 10^{12}\,\mathrm{GeV}$ using binaries with total masses $M\sim10^4$-$10^5\,M_\odot$, assuming conservative DM densities consistent with the central values of Navarro-Frenk-White profiles. Allowing for higher background densities and different extreme-mass-ratio configurations further extends the accessible parameter space. Moreover, we find that for a binary configuration with $M\sim10^4\,M_\odot$, $ρ_\mathrm{dm} = 10^3\,\mathrm{GeV/cm^3}$, and signal-to-noise ratio $\mathrm{SNR}\sim20$, a particle mass of $m_\mathrm{dm}\sim2.5\times10^{-16}\,\mathrm{eV}$ and decay constant $f_a\sim6.3\times10^{10}\,\mathrm{GeV}$ maximize the dephasing due to dynamical friction, enabling the recovery of the particle parameters at the percent level. These results demonstrate that LISA can place constraints on axion-like particle masses and self-interactions without requiring additional couplings to Standard Model fields.

Quantum kernel methods have emerged as a promising approach for leveraging high-dimensional feature spaces in machine learning, particularly in domains where classical kernel methods face scalability limitations. In this work, we present the first large-scale study of fidelity-quantum-kernel support vector machines for hyperspectral data classification without requiring heavy prior feature selection or dimensionality reduction. By simulating quantum kernels using tensor network contraction techniques and GPU acceleration, we overcome the computational bottlenecks traditionally associated with quantum models, achieving quadratic scaling O(n^2) in the number of qubits. Our approach enables the evaluation of quantum kernels on hyperspectral data with hundreds of spectral bands, aligning quantum feature spaces with real-world remote sensing applications. We provide an in-depth analysis of kernel bandwidth optimization, demonstrating its crucial role in mitigating exponential concentration effects and ensuring the model's ability to generalize. Experimental results on binary classification (Indian Pines and Methane Detection) and multiclass classification (Indian Pines) demonstrate that quantum kernels achieve competitive performance compared to a broad range of state-of-the-art classical baselines. As illustrative cases, on four 50-band splits selected from Indian Pines, the quantum model achieved a 78.0 pm6.2% accuracy for a binary classification task compared to 72.0 pm5.0% for the standard radial basis function (RBF) kernel. For a four-class classification task, the quantum kernel reached 83.3 pm3.1% accuracy, outperforming several state-of-the-art baselines. On five 75-band splits selected from the Methane Detection dataset, the quantum approach yielded 58.5\pm5.0% accuracy versus 55.1\pm2.5% for the classical counterpart...

We consider an inverted harmonic oscillator in the space $L^{2} (\mathbb{S})$ of square-integrable functions on the circle $\mathbb{S}$ and compute its density of states employing the stationary phase approximation. Our computation is based on an oscillatory integral representation of the Schwartz kernel of the time-evolution operator. This demonstrates thermalisation as a semi-classical manifestation of the classical Lyapunov instability -- reported earlier in [Phys. Rev. D 102, 044006; Phys. Rev. D 102, 124047] using heuristic analytic continuation. Our spectral analysis of the Hamiltonian points out and closes the conceptual and mathematical gaps in the preceding literature.

In a recent paper it was shown that all the Hilbert space formulas for quantum probabilities can be realized as functions of geometric properties of the associated projective space, but those functions were expressed using the structures of the associated Hilbert space. In this paper a direct description of all these probabilities is given as formulas involving only the geometric properties of the projective space itself without referring to the associated Hilbert space theory. In large part this depends on a projection theorem for complex projective space which is analogous to the projection theorem for Hilbert spaces. The importance of this is that this exhibits quantum probability in terms of the geometry of a Riemannian metric in a non-linear Kähler manifold without any reference to a linear Hilbert space. As such this is a part of a larger program of the geometrization of physics. This opens the possibility of generalizations of quantum theory in other similar geometric settings. The theory presented includes projective spaces of both finite and infinite dimension. Some comments explain how quantum theory based on a von Neumann algebra is compatible with this approach.

This thesis studies two distinct frontiers of quantum information processing: the fundamental physical limits of dynamical evolution and the practical realization of secure quantum communication networks.

In this work, we investigate the dynamic aspects of Bumblebee gravity via the parameterized post-Newtonian method. We find that the PPN framework is self-consistent up to 1.5PN order if and only if $λ= -ξ/2$, which corresponds to a direct coupling between the Bumblebee field $B_μ$ and the Einstein tensor. The requirement of tachyonic stability restricts the Bumblebee potential to satisfy $V''(0)=0$. In the specific case where $λ= -ξ/2$, the resulting PPN metric yields non-vanishing values for the parameters $α_1$ and $α_2$, as well as a novel PPN potential $U_B$ that exhibits a logarithmic asymptotic growth. The vanishing of the potential $U_B$ necessitates the additional constraints $ξ= κ/2$ or $V^{(3)}(0) = 0$. These results signify the presence of preferred-frame effects, a direct consequence of the Lorentz symmetry breaking in the model. In the limit of small $\ell$, we obtain $α_2 \simeq -\ell_2$, which yields a constraint of $|\ell| \lesssim 1.6\times 10^{-9}$ based on pulsar timing observations.

This Letter shows that a supersymmetric structure is inherent to the time space inverted (TSI) quantum mechanics (QM) framework, where the spatial evolution of states is generated by the operator $\hat{\mathcal{P}}^{\pm}(\hat{\mathcal{H}},\hat t;q)=\pm\sqrt{2m[\hat{\mathcal{H}}-\mathcal{\hat V}(q)]}$ [\href{https://doi.org/10.1103/PhysRevA.95.032133}{Phys. Rev. A. {\bf 95}, 032133 (2017)}], named here Momentunian, whose square-root structure that can be factorized. Such factorization leads directly to a supersymmetric algebra with supercharges and partner Hamiltonians. For the relativistic Momentunian the zero mode states are shown to be evanescent states, \textit{independent} of the physical potential. Furthermore, the existence of non-relativistic and relativistic Momentunian \textit{partners} is demonstrated, whose zero-mode states are no longer necessarily zero energies, but vanishing momenta states. The natural emergence of the $1/2$-fractional time derivatives in the TSI QM, leads to supercharges which incorporate memory effects into the supersymmetric wave functions. Results indicate that supersymmetry emerges as a structural property of the TSI QM rather than being imposed phenomenologically.

We study the Joule-Thomson expansion and extended thermodynamics of a modified black hole characterised by the parameters $α$, $β$, and $σ$. Analysis of the Hawking temperature, Joule-Thomson coefficient, inversion curves, and isenthalpic trajectories shows that these parameters significantly modify the heating-cooling behaviour and thermal stability of the system. The deformation parameter $α$ and control parameter $β$ shift the temperature minimum, enlarge the cooling region, and raise the inversion temperature, while $σ$ produces a weaker but consistent influence. The heat-engine analysis reveals that $α$ enhances efficiency, whereas higher $β$ and $σ$ reduce it. Overall, the results demonstrate that geometric deformation and quintessence jointly govern the unified thermodynamic structure of the black hole.

Searches for axion and axionlike dark matter based on solid-state spin qubits are fundamentally limited by strong longitudinal dephasing, which rapidly suppresses the sensitivity gains offered by entanglement. Here we show that quantum error correction (QEC) can substantially enhance axion search sensitivity in realistic semiconductor spin qubit platforms by mitigating this dominant noise source. By integrating an optimally chosen repetition code QEC with logical GHZ block entanglement, we derive closed-form expressions for the quantum Fisher information that explicitly incorporate the finite coherence time of the axion field. Our analysis demonstrates that modest QEC cycle frequencies are sufficient to significantly reduce the effective dephasing rate, thereby restoring a broad parameter regime in which entanglement-enhanced sensing surpasses the standard quantum limit. Projecting these results onto CMOS-compatible device parameters, we find that QEC-protected entangled sensing can revive otherwise inaccessible quantum advantages, yielding up to order-of-magnitude improvements in sensitivity to the axion-electron coupling $g_{ae}$. These results establish a practical and theoretically controlled pathway for using QEC to improve qubit array searches for physics beyond the Standard Model.

The nonadditivity of channel capacity is a defining feature that distinguishes quantum communication from classical communication. In the quantum realm, the channel capacity is determined by coherent information, which is defined through the von Neumann entropies of the output and its environment. Despite its fundamental importance, experimental evidence of such nonadditive quantum communication has been elusive because of the complexity of the required quantum channel. Here, we experimentally observe entropic-singularity-induced coherent-information nonadditivity using the qutrit platypus channel implemented on a photonic platform. By preparing six-dimensional photonic entanglement, we directly measure the coherent information of a platypus channel, a qubit amplitude damping channel, and their joint uses, revealing a clear violation of additivity. Quantum process tomography further reveals the entropic singularity responsible for this effect, demonstrating how singular entropy landscapes in low-dimensional channels can enhance quantum communication beyond additive limits.

We present a quantum attack on ML-KEM and related 2-power cyclotomic lattice schemes. Combining with Parts I-III, we provide an algorithm and verify the resulting approximation factor satisfies $γ\le 21 < q/2=1665$ for ML-KEM-1024, with a success probability $\ge 0.99$. We apply a tower decomposition of the Principal Ideal Problem (PIP) through the chain $\Q \subset \Q(ζ_8) \subset \cdots \subset \Q(ζ_{2^k})$ which yields a polynomial-time quantum algorithm costing $O(n^3 \log^2 n)$ gates, $O(n^2 \log n)$ qubits, and $\mathrm{poly}(n)$ classical bit operations. We extend the analysis to Falcon, Hawk, and NTRU over 2-power cyclotomic rings. This means that ML-KEM, Falcon, Hawk, NTRU-HPS, and NTRU-HRSS with all standardized parameter sets are broken under quantum attack.

We prove that the $L^2$ CVP distance from a random short ring element to the log-unit lattice of $\Q(ζ_{2^k})$ converges to $\fracπ{2\sqrt{6}}\sqrt{n}$ as $n=2^{k-1}\to\infty$. We then show that this target lies inside the Voronoi cell of the origin for $k\ge 4$. For the $L^\infty$ norm, the maximum over $n$ sub-Gaussian coordinates yields $O(\sqrt{\log n})$ which translates into a sub-polynomial approximation factor for the Short Generator Problem. We show a Coarse Lattice Theorem that Babai's algorithm returns zero for all structured targets, yet exactly recovers unit perturbations of arbitrary size. For module determinant ideals, we further prove the Trigamma Theorem that proves an intrinsic imbalance $σ_{g_0}=O(1)$ independent of the modulus $q$. Finally, combined with Parts I and II, we reduce the CDPR factor for ML-KEM from $\exp(\tO(\sqrt{n}))$ to a sub-polynomial value.

We prove a threshold-sharp stability theory for the conformal scalar-curvature sector on zero-curvature Carter backgrounds. The main result is a fully closed bounded-slab theorem: the reflecting evolution is constructed, the conserved energy is proved positive, the complete affine threshold obstruction is identified, and all remaining finite-energy dynamics are shown to be uniformly stable with no unstable modes. This is the sharp statement for compact reflecting slabs, where genuine time decay is false in general. We then extend the same threshold philosophy to black-hole exteriors, separating the intrinsic conformal mechanism from the exterior scalar-wave inputs needed for red-shift, local energy, limiting absorption, and zero-frequency control. The framework gives main applications to Kerr, Reissner-Nordström, slowly rotating weakly charged Kerr-Newman wall exteriors, and extremal horizon-charge obstructions. Our precise result is that it proves stability only for the conformal scalar-curvature sector, not tensorial or nonlinear gravitational stability, and it distinguishes boundedness, qualitative local decay, polynomial decay, and extremal Aretakis-type obstruction without conflating them.

Teleportation through a non-maximally entangled pair, e.g., $\ket{ψ(θ)}_{AB}=\cosθ\ket{00}+\sinθ\ket{11}$, induces a noisy channel and cannot achieve deterministic unit-fidelity transmission unless $θ=π/4$. We introduce a framework of \emph{assisted quantum teleportation} in which a third party (the ``Bank'') supplies auxiliary multipartite entanglement to restore a perfect Bell pair on the original $AB$ registers. We analyze two operational roles for the Bank: a Bank-measures model (measurement and broadcast) and a transfer model (the Bank transfers its subsystem and then leaves). For GHZ-class and W-class assistance we derive explicit feasibility regions for deterministic restoration and show an operational inequivalence for W resources. We further characterize finite-shot optimal success probabilities for probabilistic restoration and formulate Bank-measures feasibility for general pure Bank resources as a minimax optimization.

Non-classical photon sources made of semiconductor quantum dots (QDs) emitting in the telecommunication C-band are crucial components for low-loss, long-distance photonic quantum communication networks. Here we designed and fabricated strain--free In$_{0.7}$Ga$_{0.3}$As/In$_{0.7}$Al$_{0.3}$As QDs grown on GaAs(111)A substrates working as single-photon emitters in the 1550 nm window. The QDs were grown via local droplet etching method in a molecular beam epitaxy environment, employing a thin In$_{0.7}$Al$_{0.3}$As metamorphic buffer layer with the same lattice constant of the QD material, thus allowing for a completely strain--free self-assembly of the QDs. The QDs exhibit a C$_{3v}$ symmetry with a ground state emission in the 1400--1600 nm range. The exciton lifetimes of $\approx$ 1.3--1.9 ns and linewidths as low as $\approx$ 300 $μ$eV show the good quality of the fabricated QDs. Second-order autocorrelation measurements under pulsed excitation confirmed the single-photon purity of the emitters, yielding a $g^{(2)}(0)$ value of $0.141 \pm 0.027$

Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the classical decoding algorithm that interprets noisy syndrome measurements. Among all possible decoding strategies, maximum likelihood decoding (MLD) is provably optimal, since it identifies the logical group with largest likelihood by summing over all possible errors within logical class consistent with the observed syndrome. Despite its optimality, MLD is computationally intractable in general (#P-hard), motivating a rich landscape of exact and approximate algorithms. In this topical review, we provide a unified perspective on MLD by surveying recent advances through three complementary lenses: statistical mechanics, tensor networks, and artificial intelligence. From the statistical mechanics viewpoint, the MLD problem maps onto evaluating partition functions of disordered spin models, enabling exact solutions for certain codes and noise models as well as threshold estimation via phase-transition analysis. From the tensor network perspective, approximate contraction of tensor networks on the code's factor graph yields decoders that closely approach MLD accuracy with polynomial computational cost. From the artificial intelligence perspective, neural-network-based decoders, including autoregressive generative models and recurrent transformers, learn to approximate the MLD distribution from data, achieving high accuracy with the parallelism afforded by modern hardware accelerators. We discuss the connections among these three approaches, review their application to both simulated and experimental quantum hardware, and outline open challenges including real-time decoding, scalability to large code distances, and generalization to high-rate quantum low-density parity-check codes.

Marine oil spills require rapid detection to mitigate severe ecological and economic damage. While satellite-based Synthetic Aperture Radar (SAR) provides essential all-weather monitoring, analyzing this data remains challenging. Deep learning models often require massive datasets and incur high latency. To address this, a pixel-wise quantum-assisted Support Vector Machine (QSVM) bagging ensemble is developed. Quantum annealing is leveraged to optimize the support vectors of individual weak SVMs on small data subsets, which are then classically aggregated. The approach is evaluated on Sentinel-1 imagery using both quantum simulation and physical quantum annealing hardware. The quantum-assisted pipeline achieved performance comparable to a rigorous classical baseline, yielding an Intersection-over-Union (IoU) of 0.60 and a balanced accuracy of 0.89. Complementary experiments with gate-based quantum computing demonstrated similar segmentation accuracy, although the annealing approach offered superior inference efficiency. Generalization was further assessed on independent oil spill imagery from the Strait of Hormuz, demonstrating the potential transferability of the trained pipeline to geographically distinct spill events. These results establish the feasibility of quantum-assisted, segmentation pipelines for near-real-time environmental monitoring.

We use nearly parallel pure states to characterize positive linear functionals $φ$ on $\mathbb{M}_n$ as positive multiples of the trace if and only if $φ(A \natural B) \leq \sqrt{φ(A) φ(B)}$ for all positive definite matrices $A$ and $B$. Here $A \natural B = (A^{-1} \# B)^{1/2} A (A^{-1} \# B)^{1/2}$ represents the spectral geometric mean. For further clarification, we establish novel characterizations through the inequality $φ(A \natural B) \leq φ((A+B)/2)$ for all positive definite matrices $A$ and $B$. We also present a trace inequality related to quantum fidelity that applies to all positive definite matrices, and demonstrate that it does not characterize the trace.

Quantum error correction (QEC) is essential for building fault-tolerant quantum computers, requiring decoders that are simultaneously accurate, fast, and scalable. Most state-of-the-art neural decoders achieve high accuracy but process the full dense syndrome array of size $O(d^2 R) $regardless of the actual error rate, where d is the code distance and R is the number of measurement rounds. At physically relevant error rates (p ~ 0.1%), fewer than 5% of syndrome entries contain active detection events -- yet existing decoders process the entire syndrome volume. We introduce the Sparse Mamba Decoder (SMD), a defect-centric neural decoder that processes only the k active detection events using a 13-dimensional feature representation per defect and a Mamba state-space backbone, achieving $O(k)$ complexity. Across depolarizing, uniform circuit-level, SI1000, and Google Sycamore experimental benchmarks, SMD reduces the MWPM logical error rate by up to 49% at $d \le 5$ under SI1000 noise, runs 95-467x faster than the Tesseract near-MLD decoder and 232-463x faster than Belief Matching, and maintains nearly constant latency (24-57 us) across d = 3-9 under uniform circuit-level noise. On the Sycamore experimental dataset, the SMD ensemble matches or slightly surpasses the dense Mamba decoder of Varbanov et al. All results are obtained on commodity NVIDIA GPUs with 7.5-16M parameters, without specialized accelerators.

Unit commitment is an important optimization problem in power system operations, classified as NP-hard. This paper presents a hybrid quantum-classical method for the unit commitment problem with time-dependent constraints, where decisions must be made about which generators to turn on/off and how much power they should produce over a planning horizon. We use a hybrid quantum-classical optimization procedure to determine the on/off schedules of the generating units and the corresponding power dispatch that satisfies operational constraints such as load balance, generator limits, ramping, and reserve requirements. We frame the optimization loop as a leader-follower structure, where the quantum optimizer leads to give the on/off decisions, and the classical optimizer follows to produce the power level schedule. Leveraging Pauli-Correlation Encoding, our method scales to horizon-wide unit commitment schedules by encoding the binary variables with far fewer qubits. By combining these components, the method can handle multi-period settings while using far fewer qubits than straightforward quantum encodings that allocate one qubit per decision variable as in prior approaches. We evaluate the approach on both small- and large-scale instances, up to 312 binary variables, and show that it reliably produces feasible schedules with competitive operating costs.

Exact-binary encoding compiles a discrete cost function network (CFN) into a higher-order unconstrained binary optimization (HUBO) problem whose maximum monomial degree grows with the cardinalities of the underlying CFN variables. Given that quantum optimization hardware generally favours quadratic unconstrained binary optimization or low-degree HUBO Hamiltonians, high-cardinality CFNs therefore incur substantial overhead in the form of circuit depth, or ancilla qubits when degree-reduction techniques are employed. To ameliorate these issues, we propose \textit{truncated-binary encoding} (TBE): a modification of exact-binary encoding in which Ising-basis monomials exceeding a chosen cutoff $k_\text{max}$ are dropped from the encoded cost. We establish a tight $L^\infty$ bound on the truncation error in terms of the omitted couplings, derive sufficient conditions on the energy gap and on the single bit-flip basin barrier under which TBE preserves the global minimum and its local-minimum structure, and characterize a noise floor condition on the spectral profile under which the truncation residual acts as a perturbative correction to the underlying landscape. We then express the encoded coefficients directly as Walsh transforms of the underlying CFN cost tables, and prove a bound under which smoothness of each cost table implies rapid decay of its high-degree Walsh mass. Together these results yield a principled \textit{a priori} criterion for selecting $k_\text{max}$ and for judging whether a given CFN admits a small-$k_\text{max}$ TBE.

Quantum-mechanical scattering states are energy eigenstates obeying particular boundary conditions, whose behavior at infinity encodes the S-matrix which defines the outcoming of scattering experiments. With an eye toward numerical algorithms for computing nonrelativistic S-matrices, we present a family of stable minimum principles for scattering states. States that approximately satisfy these minimum principles are shown to have a bounded difference with the true scattering states. These minimum principles and stability estimates can be used to obtain rigorous bounds on scattering amplitudes. We show that these minimum principles are applicable to momentum-dependent potentials, long-range (Coulomb) interactions, and elastic or inelastic scattering of bound states.

We develop a mathematically rigorous framework for simulating \emph{multiscale physical systems} using quantum computational resources, by translating the \emph{language of asymptotic-preserving (AP) schemes} into the formalism of quantum channels and Lindbladian dynamics. For stiff open quantum systems governed by singularly perturbed generators $\cL_\eps = \eps^{-1}\cL_{\mathrm{fast}} + \cL_{\mathrm{slow}}$ with $\eps \to 0$, we prove that layered quantum protocols, which implement fast-scale relaxation via native analog evolution or analytic manifold projection, converge uniformly in the diamond norm to consistent discretizations of the limiting slow dynamics, with explicit error bound $\mathcal{O}(\epsΔt + Δt^2)$ independent of stiffness. We establish precise resource-complexity bounds showing that superlinear gate-count savings $Ω(κ\cdot(d_{\mathrm{tot}}/d_{\mathrm{slow}})^c)$ arise if and only if fast dynamics are resolved via (i) hardware-native analog evolution, or (ii) analytic adiabatic elimination reducing effective Hilbert space dimension. The framework is illustrated through cavity QED in the bad-cavity limit and a quantum-inspired AP discretization of kinetic equations converging to fluid limits, with quantified error propagation in trace and diamond norms. This work provides a principled mathematical bridge between classical multiscale numerical analysis and quantum simulation algorithms.

We study the nonlocal effective action of a massless scalar field defined on a flat manifold with a curved boundary. Using a heat-kernel approach, we derive a covariant expansion of the nonlocal contribution to quadratic order in the extrinsic curvature tensor. Our construction provides a geometric framework that both reproduces earlier results obtained for Monge-patch embeddings and extends them to more general surfaces that need not admit a global Monge-patch description. The expansion is valid in the regime where gradients of the extrinsic curvature dominate over nonlinear curvature effects. As an application, we compute the particle-creation rate for an oscillating deformed ring in $2+1$ dimensions and an oscillating deformed sphere in $3+1$ dimensions.

We investigate thick accretion disks (Polish Doughnuts) around rotating self-interacting boson stars in general relativity and scalar-tensor theories, focusing on spontaneously scalarized solutions and their general relativistic counterparts. Using equilibrium models with constant specific angular momentum, we analyze disk structures across the parameter space, with emphasis on the phase transition between GR and scalarized configurations. We find that scalarization induces qualitative changes in the spacetime that significantly affect disk morphology. In particular, scalarized boson stars can lack innermost circular orbits, allowing stable motion down to the center and enabling highly compact, quasi-spherical disks. For the most massive scalarized solutions, a non-monotonic angular momentum profile further permits two-centered disk configurations connected by a cusp. Overall, disks around scalarized boson stars are more compact and more strongly bound than those in general relativity, highlighting distinctive features that may serve as observational signatures of alternative gravity theories in the strong-field regime.

This paper describes the setup and the results of the direct on-wafer measurements of a FET noise parameters obtained with a source-pull method at temperatures down to T=4K and in the 5-12 GHz frequency range. The setup consists of a cryostat with wafer probes, two reflectometers, a programmable impedance generator, wideband isolators and bias tees and low noise preamplifier, all cooled to cryogenic temperatures, allowing to perform a full vector error-corrected wafer-level measurements of the discrete transistors and amplifier dies. The setup and its calibration procedure are designed in a such way that allows simultaneous calibration, S-parameters, noise parameters and I-V curve measurements of several FETs all in one cooldown. Using the described setup we perform first measurements of 14nm FinFETs and also measure noise parameters of an LNA based on these FETs. Resulting noise temperature values are compared against those obtained using independent and alternative measurement techniques.

Variational compression can significantly lower implementation overheads for encoding the time evolution of Hamiltonians into quantum circuits. However, they usually lack global convergence guarantees and well-established scaling behavior. In this work, we provide a recipe for choosing the initial point of such variational optimizations that guarantees convergence to a quantum circuit with near-optimal gate complexity $\mathcal{O}\left( N \, t \, \text{polylog}(N \, t/ε) \right)$ for all local and translationally invariant Hamiltonians. We demonstrate our method by encoding the globally controlled time evolution of a Heisenberg antiferromagnet on a Kagome lattice. For $N = 48$ sites, evolution time $t=0.1$ and infidelity $ε\approx1\%$, the controlled time-evolution circuit requires 960 two-qubit B gates, for which we propose a straightforward implementation scheme for ion-trap setups. Thereby, our recipe extends digital quantum simulators toward system sizes and geometries that are challenging for classical computation.

The August 2024 finalisation of FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA) closed the algorithmic gap in post-quantum cryptography (PQC). The production gap -- hybrid combiners, versioned key formats, protocol helpers, and migration tooling -- remains open. We present quantum-safe, a Python library that closes all three critical gaps we identify, and a systematic evaluation of the nine-library ecosystem that quantifies them. We score nine PQC libraries across eight production-readiness dimensions. Three dimensions have coverage below 35%: hybrid KEM support (11%), migration tooling (22%), and protocol integration (33%). quantum-safe scores Full on all eight. The full API reduces the hybrid KEM task from 45 lines of manual combiner code to three lines, directly lowering the risk of insecure combiner implementations. We report the first statistically rigorous per-operation overhead measurement for a Python hybrid PQC library (3,000 iterations, CPU-pinned, bootstrapped 95% confidence intervals). A full X25519 + ML-KEM-768 handshake completes in 243 μs under Docker/Linux -- 0.5--2.5% of a typical TLS 1.3 round-trip budget. At 5,000 concurrent users, throughput holds at 2,848 ops/s with only 4.9% degradation versus the single-user baseline, confirming that liboqs releases the Python GIL during C-level operations. We introduce Coefficient of Variation (CoV) as a practical timing side-channel proxy across all FIPS 203/204 operations. ML-KEM-768 decapsulation achieves CoV = 3.9%, within the AES-256-GCM noise floor (2.1%). ML-DSA-65 signing shows CoV = 51.5%, expected from FIPS 204 rejection sampling, not a side-channel. This CoV methodology has not previously been applied to PQC library evaluation and provides a lightweight complement to formal constant-time verification tools. All results are reproducible via a single Docker command.

Magnetars are among the most extreme laboratories in the universe, harboring surface magnetic fields reaching $10^{15}$~G. At these supercritical scales, Maxwell's linear electrodynamics is superseded by Nonlinear Electrodynamics (NLED). While vacuum birefringence has provided initial observational evidence for these effects, its broader impact on photon propagation remains largely unexplored. In this work, we demonstrate that NLED significantly alters photon propagation in the vicinity of magnetars, deviating light from standard null-geodesics. We estimate that neglecting these corrections leads to relative errors in inferred stellar radii by means of ray-tracing techniques of approximately $10\%$. Furthermore, we find that NLED induces a systematic minimal travel-time delay of approximately $350~n$s, a value that already far exceeds the $100$~ns temporal resolution of missions like NICER. These results are critical for the interpretation of X-ray pulse profiles from current and future observatories, such as eXTP, which rely on high-precision light-bending and timing models to determine neutron-star masses and radii. Finally, our results underscore the role of magnetars as a vital window into the physics of superdense matter and supercritical fields, and we briefly highlight other astrophysical observables--such as glitches and antiglitches--that may be affected by NLED.

We obtain the exact many-body density operator of a boundary-driven XXZ quantum circuit via a spatially inhomogeneous matrix product Ansatz. The Ansatz has formally infinite bond-dimension and generalizes authors' previous construction \cite{2025XXZcircuit} for the XXZ interactions. The boundary qubits are coupled to reset quantum channels that project them toward arbitrary pure target states. We find and describe a family of relatively robust separable chiral nonequilibrium steady states (NESS), which are elliptic analogs of spin helices for the circuit, and which are particularly attractive from an experimental perspective.

Robustness in dissipative light-matter systems has recently been associated with resonance conditions or geodesic evolution. We show that, in the nonlinear Jaynes-Cummings model, these conditions are necessary but not sufficient. Using a Kerr-type extension together with a Lindblad description of cavity losses and atomic decoherence, we identify a dynamically enabled mechanism in which the stability of geometric phases and entanglement is governed by the alignment between coherent and dissipative trajectories in Hilbert space. Our results reveal that environmental action does not merely suppress quantum features, but reshapes the geometry of state-space evolution: protection emerges only when dissipation preserves the structure of the underlying unitary dynamics. This establishes a general geometric criterion for decoherence resilience in nonlinear light-matter systems and provides guiding principles for engineering protected evolution in open quantum platforms.

The Quantum Fourier Transform (QFT) is required by hidden subgroup problem (HSP) algorithms, including Shor's algorithm for factoring. The circuit depth of the QFT remains challenging for near-term hardware. To find shallower alternatives we identify two properties that are exploited by the QFT to enable HSP. Firstly, the shift invariance of the QFT allows for the removal of a random overall shift. Secondly, the QFT retains information about the hidden subgroup generator accessible in the measurement outcomes. We quantify that information via the discrete Fisher information. We construct a family of shallow circuits using Hadamards and controlled-Phase gates, HP-$L$ circuits, that we prove preserve shift invariance. Numerical analysis shows these circuits retain exponentially growing Fisher information. The $\mathcal{O}(n)$ HP-$1$ can replace the $\mathcal{O}(n^2)$ QFT in Shor's algorithm, as demonstrated numerically, with an efficient neural network implementing classical post-processing.

Bayesian quantum estimation provides a robust framework for quantum technologies, especially in scenarios with limited data and minimal prior information. Yet, its application to continuous-variable Gaussian systems has remained limited and largely numerical due to the complexity of the underlying parameter integrals. Here, we introduce a variational framework reducing the optimisation over measurements and estimators to a finite-dimensional linear problem and admitting closed-form solutions. This is achieved by restricting the analysis to operators polynomial in the canonical quadratures, leading to solutions with a geometric interpretation as orthogonal projections of the global optimum. We further derive a necessary and sufficient condition for global optimality. Through single-shot examples, we show that the framework yields experimentally feasible strategies based on Gaussian operations and quadrature measurements that are either optimal or near-optimal, and that replacing the induced estimator with the posterior mean further improves performance towards the global optimum.

Trying to solve hard optimisation problems with quantum techniques requires transformations of domain objectives and constraints into formats compatible with a chosen quantum algorithm. This often introduces inefficiencies and overheads that limit or even endanger potential quantum advantage for current and future approaches. To understand and mitigate these inefficiencies, software toolchains are essential for implementing transformations, analysing overheads and eventually selecting optimal transformation paths. Decoded Quantum Interferometry (DQI) is a novel approach that achieves apparent quantum advantage for certain algebraic optimisation problems. It natively operates on Max-LINSAT, which is unusual for combinatorial optimisation, and creates the need for software solutions that alleviate the burden of manually transforming problems of interest into this format. We present DQI-Kit, a software framework that provides a unified, extensible interface for automatically encoding constrained optimisation problems into Max-LINSAT. Users can describe the various types of objectives and constraints that are common in industrial optimisation problems. Our framework converts these into Max-LINSAT instances via a series of problem transformations and computes an estimate of the expected performance of DQI on these instances. We provide an initial analysis of the implemented transformations, discussing inefficiencies and ways to mitigate them. DQI-Kit is the basis for our ultimate goal of establishing a standardised framework that will enable further investigations to identify practical use cases for which quantum advantage with DQI can be achieved.

Polarization of the cosmic microwave background (CMB) brings out information not only on the early universe but also on the late-time large-scale structure via weak gravitational lensing. Here, we show that circular polarization is induced in principle from CMB temperature fluctuations when the optical Magnus effect is incorporated into gravitational lensing. This is a consequence of the transverse shift of a trajectory of light depending on its helicity that requires right-handed and left-handed components at the same observation point to be sourced from different points of the surface of last scattering. Whereas the resulting circular polarization is found far beyond the scope of current detection, our work establishes the optical Magnus effect on gravitational lensing as a new fundamental mechanism to produce circular polarization of CMB.

We present a hybrid adiabatic algorithm for maximum independent set (MIS) using Rydberg atom arrays. We engineer local controls that preferentially excite atoms with few neighbors, which represent graph nodes with small degrees. Numerical simulations show that the designed pulses accelerate convergence to the MIS state and suppress population in trap states. We obtain higher success probabilities than traditional global controls and a $25\%$ reduction in fidelity decay rate as problem hardness increases.

In the current research, we have reported the Tsallis Holographic Dark Energy (THDE) (\textit{JCAP}, 2018(12), p.012.) model reconstructed within the framework of $f(Q, C)$ gravity (\textit{JCAP}, 2024(03), p.050.), combining entropy-based dark energy models with geometrically motivated modified gravity to explain late-time cosmic acceleration. The reconstructed model is found to exhibit significant sensitivity to the parameter space $(H_0, a_0, n, δ, ζ,r_d)$ and the initial conditions. The evolution of the equation of state and deceleration parameters is found to be highly dependent on these parameters. A comprehensive Markov Chain Monte Carlo analysis using observational datasets comprising {CC+Pantheon$^{+}$+DESI DR2} was performed, yielding best-fit values that demonstrate strong consistency with observational data, which is further validated for its consistency through the computation of the age of the Universe. The evolution of the jerk and snap parameters is examined and compared with the $Λ$CDM prediction. Statefinder diagnostics, through the evolutionary trajectories of the pairs $(r, s)$ and $(r, q)$ are derived and indicate that the model passes through the $Λ$CDM fixed point and the physical viability of the model is further consolidated through analysis of the four energy conditions.

Complete quantum charging provides a sharp setting in which to ask how much multipartite entanglement is forced by speed itself. For a closed \(N\)-qubit battery evolving from \(\ket{\downarrow}^{\otimes N}\) to \(\ket{\uparrow}^{\otimes N}\) under a time-independent Hamiltonian, we exactly solve the pure-state depth-constrained speed problem. If the realized trajectory has entanglement depth at most \(k\), then the largest possible QSL-normalized rate \(η=τ_{\rm QSL}/T\) is \(η_{\max}(k)=\lceil N/k\rceil^{-1/2}\). Conversely, an observed rate \(η\) certifies trajectory entanglement depth at least \(\bigl\lceil N/\lfloor η^{-2}\rfloor\bigr\rceil\). The mechanism is block orthogonalization: under a fixed product partition, complete charging forces all blocks to orthogonalize simultaneously, and the quantum speed limit converts this counting constraint into the speed bound. Balanced cluster-flip evolutions saturate the bound, establishing an exact integer staircase frontier. Thus fast complete charging cannot be explained by many small independently charging blocks; in particular, crossing the threshold \(η>1/\sqrt2\) certifies, for \(N>1\), the generation of genuine \(N\)-partite entanglement.

Photodetection converts optical quantum states into measurement events, but the usual electric-field response model becomes restrictive when the detector response is shaped by cavity, superconducting, or metamaterial engineering. We develop a generalized quantum photodetection framework in which electric and magnetic field amplitudes contribute coherently to the detection operator, and analyze it in a far-field two-source geometry, a two-mode single-photon setting, and a lossy resonant detector model. The far-field reference case exhibits complete detector-amplitude cancellation, absent in the electric-only Glauber response, while the single-photon model shows that the detector continuously rotates the effective measurement basis and controls the first-order visibility via an exact closed-form law. In the resonant realization, a monitored radiative output channel can be dark while the detector remains internally excited and absorptive, with unit absorption of the matched input mode at critical coupling. These results identify basis-selective readout and channel-selective absorption as experimentally relevant signatures of engineered electric-magnetic photodetection.

We investigate the warm inflationary scenario in the Weyl geometric gravity theory, in which the action is constructed by adding matter to the simplest conformally invariant gravitational action in Weyl geometry. The $\tilde{R}^2$ theory can be formulated equivalently as a linear theory supplemented by an additional scalar degree of freedom originating from higher-order curvature terms, with the equations of motion obtained via variational methods. We investigate the cosmological implications of the theory by considering the warm inflationary scenario of the early evolution of the Universe, in which radiation, the inflaton field, and the Weyl vector coexist. We consider the widely studied linear dissipation coefficient model along with a quartic potential, and investigate the influence of the Weyl vector term on the dynamics. We have performed numerical computations for different coupling models, and we have successfully developed a warm inflationary model in which the Universe transitions naturally from an inflationary epoch to a radiation-dominated era. The relevant cosmological observables have been calculated and compared with the latest observational constraints from the ACT data.

In this paper, we investigate background-hierarchy bounds in Type~3 of New General Relativity (NGR). These bounds arise when the contribution associated with the evolution of the background spacetime exceeds that of the quadratic kinetic term in the perturbed Lagrangian. Type~3 of NGR has two free parameters and preserves diffeomorphism invariance and spatial rotations, while breaking Lorentz-boost invariance. We first review Type~3 and identify preferable gauge choices for metric-affine gauge theories of gravity with Weitzenböck connection, including NGR, from the viewpoint of symmetry in both Dirac--Bergmann analysis and linear perturbation theory. We then revisit the perturbative analysis of Type~3 and show that the propagating modes are correctly identified even when the perturbed Lagrangian is not written solely in terms of gauge-invariant variables. Finally, we derive the background-hierarchy bounds for the scalar, transverse-vector, and tensor modes around a flat FLRW background, and identify the region of parameter space in which the linear perturbation theory of Type~3 remains viable for cosmological applications.

We study the separable master equations for the electromagnetic and gravitational perturbations in five-dimensional Myers-Perry black holes. In the static limit, while the master equation for the electric polarization of the Maxwell field reduces to that of a massless scalar field, the magnetic polarization and gravitational perturbation yield Heun equations for both its angular and radial components. Remarkably, these Heun equations fall into a special class that admits exact analytic solutions in terms of hypergeometric functions. We reconstruct the gauge field using master fields and study its asymptotic behavior. When expanding the result in the basis of modified spherical harmonics, we find modes with higher angular momentum arise in response to the excitation of sources with lower angular momentum. The static tidal Love tensor that characterizes such mixing structure of the response can be computed iteratively. We also discuss the possible near zone approximation of the master equations for the magnetic polarization.

In this paper, we develop an extension of the Central Limit Theorem (CLT) to the setting of bosonic quantum channels. This extension provides a deeper understanding of Gaussian bosonic channels as extremal objects. Using our CLT for bosonic quantum channels, we recover both the classical CLT and the CLT for bosonic quantum states, thereby offering a unified perspective that connects classical probability theory with continuous-variable quantum systems. Moreover, using our result, we can provide necessary uncertainty relations that every physical (possibly non-Gaussian) bosonic quantum channel must satisfy. As another application of our limit theorems, we derive tight lower bounds on the energy-constrained quantum capacity of linear bosonic channels by relating it to the capacity of their associated Gaussian bosonic channels, further reinforcing the role of Gaussian channels as extremal.

In this study, we precisely evaluated the feasibility of the chaotic inflation model within a non-canonical kinetic framework. By applying the slow-roll approximation and imposing constraints on the equilateral non-Gaussianity $f_{\rm NL}^{\rm equil}$, we imposed constraints on the feasible range of the potential index $n$. We established physical bounds for the non-canonical parameter $α$. To obtain precise parameter constraints, we solved the primordial perturbation equations numerically and conducted a rigorous MCMC analysis by using a comprehensive joint P-ACT-LB-BK18 dataset. For these potentials $n=1/3$, $2/3$, and $1$, our results respectively tightly limit $α$ to the levels of $8.8^{+1.6}_{-2.8}$, $11.7^{+1.7}_{-2.6}$, and $16.4^{+3.7}_{-7.0}$, within the corresponding $1σ$ confidence intervals. Meanwhile, the required number of $e$-foldings naturally converges to $N \simeq 54$, without the need for fine-tuning. These findings confirm that non-standard mechanisms can resurrect excluded chaotic inflation models within the $1σ$ allowed regions of high-precision cosmological data.

We investigate the cosmological implications of unimodular gravity (UG) featuring energy diffusion and spatial curvature. While standard diffusion models often suffer from thermodynamic inconsistencies, we propose a phenomenologically viable power-law Ansatz for the diffusion function, $Q(z) = Q_0(1+z)^β$, which strictly satisfies the second law of thermodynamics by demanding positive entropy production ($βQ_0 > 0$). Using a joint statistical analysis with the Pantheon+ Type Ia Supernova compilation and Baryon Acoustic Oscillation (BAO) measurements, we tightly constrain the parameter space. We find a diffusion exponent of $β= 0.503_{-0.126}^{+0.118}$ and a slight preference for a closed spatial geometry with $Ω_{k0} = -0.109_{-0.071}^{+0.076}$ at present time. Remarkably, the consideration of spatial curvature and diffusion naturally alleviates the Hubble tension, yielding $H_0 = 73.350_{-0.226}^{+0.221}$ km/s/Mpc while maintaining a consistent cosmic age of $t_0 \simeq 13.61$ Gyr. Furthermore, the constrained diffusion scales as a stable, quintessence-like effective dark energy ($ω_{\text{eff}} \simeq -0.832$). Thus, unimodular diffusion provides a thermodynamically consistent phenomenological alternative that can alleviate the Hubble tension while preserving both the cosmic age and the sound-horizon scale, with a preference for a closed spatial geometry.

We study the quantum Fourier transform (QFT) block-encoding of the semi-discrete fractional Laplacian on bounded domains with open, zero-extension boundary conditions. In the notation of the main construction, the target operator is the finite Toeplitz truncation \(A^{(N)}_{α,h}\) obtained from the full-lattice semi-discrete operator with symbol \(|ξ|^α\). A finite QFT register, however, diagonalizes circulant matrices rather than Toeplitz truncations. The native QFT circuit therefore implements a periodic surrogate \(\widetilde A^{(N)}_{α,h}\), not the open-boundary operator. We identify this mismatch through an exact Toeplitz-to-circulant aliasing identity. To recover the open-boundary action, we zero-pad the state into a larger \(M\)-point QFT register, apply the same Fourier-symbol block-encoding, and compress back to the physical subspace. The resulting compressed block satisfies \(P_{N\to M}^{\dagger}\widetilde A^{(M)}_{α,h}P_{N\to M} = A^{(N)}_{α,h}+E^{(M)}\), where \(E^{(M)}\) is controlled by the tail of the semi-discrete convolution kernel. Thus, the QFT layer implements the fractional symbol, while zero-padding supplies the open-boundary geometry. The construction is an operator-compilation primitive for boundary-aware quantum simulation rather than a complete PDE solver.

Frequency combs enable precision measurements across timekeeping, spectroscopy, ranging and astronomy, and are now extending to integrated and field-deployable platforms. Realizing their full performance demands a comprehensive account of the quantum noise that arises when broadband optical fields are converted into finite-bandwidth electrical signals. Here we present a rigorous first-principles quantum-mechanical framework for optical frequency-comb metrology based on continuous-mode field quantization and a comb-line-resolved description of quantum fluctuations. The theory describes how quantum fluctuations of the comb field are transduced into electrical measurement noise. We apply the framework to two canonical settings, optical frequency division (OFD) and dual-comb spectroscopy (DCS), where it reveals two effects beyond semiclassical reach: a dependence of the OFD standard quantum limit on the comb spectral envelope, and a cyclostationary noise penalty that obstructs straightforward squeezing in DCS. These insights identify practical, resource-efficient routes to quantum enhancement through engineered comb states, laying a foundation for the design of next-generation frequency combs operating at and beyond standard quantum limits.

Quantum state preparation is a fundamental primitive in quantum algorithms for encoding classical data into quantum amplitudes. We compare the cost of preparing general $n$-qubit states with real amplitudes using two common paradigms: rotation-based methods, based on controlled rotations, and sampling-based methods, based on a structured representation of the target state. Although these approaches are often theoretically compared using CNOT count and $T$-count, their relative performance in total gate count remains less well understood practically. We compare representative rotation-based and sampling-based methods using $T$-count and total gate count, and analyze how compilation overhead affects their relative performance. We also develop a software package for compiling state preparation circuits, designed as a practical subroutine for more general quantum computations. Numerical experiments on resource states and quantum states related to quantum chemistry, condensed matter physics, and simulation via Magnus expansion over a range of target accuracies $ε$ support the analysis. Our results show that sampling-based methods achieve asymptotically lower $T$-count and retain an overall advantage after accounting for total gate count and compilation overhead.

We present several refinements and extensions of the statistical quantum phase estimation (SQPE) framework to address some of its key practical limitations, improving its applicability to realistic cases. Recently, a family of statistical approaches for QPE have been proposed where each run uses only a few ancillae and shorter circuits than standard QPE and thus is better suited for early fault-tolerant quantum computers that are qubit-and depth-limited. SQPE method within that family estimates the cumulative distribution function (CDF) associated with spectral density of the Hamiltonian for a given trial state by using its Fourier approximation and then identifies the first jump discontinuity of the CDF to determine the ground state energy (GSE) of the Hamiltonian. It relies on random compilation procedure based on linear combination of unitaries (LCU) decomposition of the Hamiltonian assuming positive Pauli weights and requires a good estimate of lower bound on the overlap between the trial and true ground state, both of which may be difficult to achieve in practice. We address these limitations by generalizing the random compilation procedure for negative Pauli weights and employing a changepoint detection method for determining GSE which does not rely on an estimate of this overlap. We also show that by exploiting symmetry of the Fourier series one can reduce number of circuit runs/samples by a factor of 2x while keeping the GSE estimation accuracy the same. We illustrate these new developments numerically via a quantum simulator in Qiskit.

We investigate whether a timelike naked singularity of negative-mass Schwarzschild type can be causally dressed by a static anisotropic matter distribution in classical general relativity. Working within a spherically symmetric framework, we solve Einstein's equations for a general density profile \(ρ(r)\) and show that the horizon structure is governed by the auxiliary function \(Φ(r)=2m(r)-r\), whose zeros determine the existence and multiplicity of horizons. We derive sufficient conditions for the formation of a unique outer event horizon in terms of the total added mass, the localization of the matter profile, and the monotonic behavior of the effective compactness function \(8πr^2ρ(r)\). In particular, non-negative and sufficiently localized density profiles can cloak the timelike singularity when the cumulative matter contribution overcomes the negative bare mass, whereas non-monotonic profiles generically lead to multi-horizon geometries. We illustrate the formalism with discontinuous and smooth power-law profiles, logarithmic branches, and T-duality-inspired limiting configurations. These results provide a sufficient-condition framework for horizon formation around timelike naked singularities and clarify how the radial organization of matter controls causal accessibility in static general relativity.

In a very recent paper, we computed the quasibound states of massless acoustic excitations interacting with a new (effective) acoustic geometry with circulation. Notably, the behavior of this acoustic black hole aligns with the phenomenology observed in recent experiments that include superfluids. Such vortex fluid flow bundles exhibit solid-body rotation at length scales larger than the inter-vortex distance, which adds some complexity to the study of quantum fluid behaviour. To theoretically deal with this issue, we present an extension of our previous results by including a solid-body rotation term at the angular fluid velocity and then we perform a spectral study by using the analytical solutions for the scalar wave equation of motion.

We introduce Magic Secret Sharing (MSS), a quantum cryptographic primitive in which the secret is the computational capability of a quantum state rather than its classical description. In the resource theory of magic, non-stabilizer states fuel universal quantum computation via non-Clifford gates; MSS distributes this resource with an (n-1,n) threshold structure using a pre-shared GHZ state and a single local phase gate P(phi) = diag(1, exp(i*phi)). Any individual party holds the maximally mixed state I/2, with Wigner distance C(I/2) = 0, so no local operation can yield non-Clifford computational advantage regardless of what operations are applied or what noise acts on the device. The authorised coalition reconstructs magic content C(phi) = (|sin(phi)| + |cos(phi)| - 1)/2 exactly, enabling a logical T gate via gate teleportation in multi-server blind quantum computation (BQC). Among diagonal parametric gates, phase gates are the unique class satisfying the security condition, characterised via an exact column-sum condition. The protocol is elevated to a one-sided device-independent (1SDI) setting via a steering inequality: the assemblage produced on the recipient's side certifies magic delivery without trusting the coalition's devices. We demonstrate the (2,3) instance on ibm_marrakesh (156-qubit IBM Heron): security (C(rho_Bob) = 0.000, below LP reconstruction tolerance) holds in all runs, and state fidelity reaches 0.959-0.986 for the authorised party, with faithfulness confirmed for all four test values of phi including near-exact recovery (C = 0.154 vs theory 0.153) for phi = pi/8.

Compiling quantum programs for fault-tolerant execution requires transforming high-level operations through multiple abstraction layers: from logical gates to error-corrected encodings to hardware-native instructions. A key challenge is that quantum error correction turns purely quantum programs into hybrid quantum-classical programs, where classical feedback from syndrome measurements drives quantum corrections at runtime. Existing compilation frameworks handle these quantum and classical components separately, requiring manual adaptation of classical logic at each compilation stage, all while preserving program semantics. We present qstack, a compiler framework built around a purely quantum intermediate representation in which classical logic is accessed only through opaque callbacks, written in any classical language. The framework's central mechanism, callback wrapping, enables compositional compilation: each compiler pass automatically adapts both quantum operations and their associated classical callbacks, and any kernel dynamically generated by a callback is compiled through the full pipeline. This allows ISA translation and quantum error correction to be expressed as composable compiler passes, including concatenation of error-correcting codes, without manual intervention. We demonstrate end-to-end compilation from a high-level gate set through Clifford gates to trapped-ion native operations, with bit-flip and phase-flip repetition codes, the Steane code, and the Shor code obtained by composing two repetition passes.

We compute second-order quantum corrections, as quantum dispersions and correlations, to a cosmological model coupling a single scalar perturbation mode to a bouncing background within Loop Quantum Cosmology (LQC). Using an effective quantization approach in which quantum moments extend the classical phase space as new dynamical degrees of freedom, and incorporating the cosmic bounce through holonomy corrections in the $μ_0$ scheme, we derive a coupled set of effective equations of motion for the expectation values and second-order quantum moments of both the gravitational and scalar sectors evolving with respect to a clock scalar field. Within the test-field approximation and for a vanishing scalar potential, the quantum moment equations reduce to a third-order ordinary differential equation for the mean squared deviation $G^{vv}$ of the Mukhanov-Sasaki variable in a de Sitter background with LQC bounce. Treating the effect of bounce as a perturbation of the solution, we construct the corresponding correction to the dimensionless curvature power spectrum. The leading correction is suppressed by the sixth power of the Planck length, producing a scale-dependent enhancement $δP_{R} \propto (k \ell_{\rm Pl})^6$ that modifies the spectral index by $δn_s \sim 6(k \ell_{\rm Pl})^6 \ll 1$ for all cosmologically observable modes, in full consistency with current observational constraints. Numerical evolution of the full coupled system reveals a conditional ultraviolet regularization of the bounce-induced spectrum: the gravitational quantum moments generate a damping mechanism that suppresses the scalar perturbation amplitude after the bounce. Including cross-sector quantum correlations amplifies perturbation modes and introduces numerical instabilities at high wavenumbers, signaling the limits of the second-order truncation in the ultraviolet.

The comparison of vibrational transition frequencies of $\mathrm{H}_2^+$ and $\overline{\mathrm{H}}_2^-$ offers a new opportunity to test CPT invariance. Myers [Phys. Rev. A 98, 010101(R) (2018)] proposed performing laser spectroscopy in a Penning trap (PT) with non-destructive read-out. Here, we provide an extensive analysis of this proposal, introduce novel aspects, and discuss its implementation in PTs that incorporate either the continuous Stern-Gerlach effect or quantum-logic spectroscopy. We derive estimates for the achievable accuracy of the test. We find that a comparison of the vibrational frequencies at a fractional level of $1\times10^{-17}$ is a realistic prospect, using technology that is mostly already available. We also analyze complementary CPT invariance tests, namely those of the g-factor of the bound electron/positron via electron-spin-resonance spectroscopy and of the magnetic moment of the proton/antiproton via radiofrequency spectroscopy.

Microfabricated ion trap chips are at the core of some of the most advanced quantum computers. How a large number of ions is arranged and controlled on an ion trap chip depends on the chosen trap architecture. One such architecture is the quantum spring array (QSA). In the QSA architecture, ion chains are arranged in a two-dimensional lattice and interact with ion chains in neighboring sites in the radial and axial directions of the respective chain. This interaction, or coupling, is mediated by the Coulomb force while keeping ions in separate trapping sites, and scales inversely with the third power of the separation. The capability to control the distance between ions in the lattice is thus essential. In previous works, the radial separation between ions was tuned by controlling the rf pseudo-potential, which revealed to be experimentally challenging to realize while maintaining low heating rates. In this work, we present an ion trap chip design that allows tuning of the radial distance between ions using only dc voltages. The radial transport is executed between different interaction zones, designated for quantum operations, through specifically designed transition zones. A prototype of this type of ion trap chip was microfabricated on fused silica substrate. Its functionality is characterized by demonstrating dc-controlled radial transport of a single ion through a transition zone and measuring stray fields and ion heating rates in the center of the trap. Moreover, the fabrication of a multi-metal layer version of such a trap is presented as a scaling path for the presented chip design.

We develop a general framework for interpreting the thermodynamic descriptions of Kerr-anti de Sitter black holes (KadS). These descriptions satisfy a first law and respect the homogeneity required by scaling properties. Additionally, they are subject to restrictions from semiclassical arguments. We show that temperature and angular velocity are kinematic quantities tied to a reference frame, identified through the Euclidean formalism. However, the pressure-volume contribution is a dynamical term that requires a gauge fixing of the potential mass and volume. It is established that the observer associated with a given thermodynamic description is directly encoded in the Killing vector that generates the horizon. We demonstrate that the quantum statistical relation restricts the infinite family of KadS descriptions to a subclass that reduces to Schwarzschild-adS and Kerr thermodynamics in the limits of vanishing cosmological constant and angular momentum. Furthermore, we establish the uniqueness of both the description associated with a frame co-rotating with infinity, and the description whose thermodynamic and geometric volumes coincide. Thus, our framework provides a coherent interpretation of the variety of KadS thermodynamics, reconciling geometric and quantum-statistical considerations.

Quantum error-correcting codes (QECCs) sit between noisy quantum hardware and reliable computation, so the code parameters used in practice must be trustworthy. The single number that summarizes a code's strength is its distance, yet certifying a distance lower bound is NP-hard in general, placing it beyond the reach of pen-and-paper proofs as well as direct proof-assistant scripting. As a result, distance values in the literature come either from non-scaling hand proofs, or from unverified solvers that leave a trust gap exactly where the code is supposed to provide a guarantee. We present Lean-QEC, the first Lean 4 formalization of stabilizer-code theory that delivers end-to-end, machine-checked distance certificates at industrial code sizes. Lean-QEC formalizes the linear algebra of qubit states, the Pauli group, stabilizer codes, the binary symplectic representation, classical coding theory, and the CSS and Bivariate Bicycle families. To break the combinatorial barrier, Lean-QEC translates the distance condition into a Boolean satisfiability formula through a verified reduction. The pipeline scales through a BitVec-flattened encoding that replaces Lean's Matrix representation, and an error-location encoding that reduces the variable count from $n$ to $k\lceil \log_2 n\rceil$. With these, we obtain automatically-generated Lean-checked distance proofs for a large range of industrially viable qLDPC codes within the Bivariate Bicycle and Generalized Bicycle families, including [[90, 8, 10]] and [[70, 6, 9]] BB codes, with the formulation scaling up to 144 qubits when performed outside the Lean kernel. The resulting library is reusable and is designed to plug into broader Lean-based efforts toward end-to-end verification of fault-tolerant quantum computation.

We examine the correspondence principle for an equiprobable superposition of high-energy eigenstates of the infinite square well using a fully analytical Fourier-based approach. We derive a closed-form asymptotic expression for the interference terms $ρ_α^{\text{a}}(x)$ by expanding them into a geometric series of quantum Fourier coefficients. We show these terms act as functional envelopes that do not vanish individually but become asymptotically equivalent in the large-$n$ limit. Furthermore, we prove the total probability density for a superposition of $2Δ+1$ states converges exactly to the uniform classical distribution as $Δ\to \infty$. Dynamically, the expectation value of position reproduces the classical triangular trajectory asymptotically. Residual quantum deviations remain confined to boundary layers whose relative width vanishes under macroscopic resolution. These results establish a rigorous asymptotic realization of the classical limit for isolated bound systems in both static and dynamical contexts.

We present a bidirectional internal squeezing scheme for gravitational-wave detectors and show that it saturates the lowest known lower bounds on quantum noise from internal optical dissipation. The scheme uses two optical parametric amplification stages inside the signal-extraction cavity that act on intra-cavity fields propagating in opposite directions. Thereby, most vacuum fields entering the interferometer are squeezed, while the signal and internal vacuum fields are amplified so that loss in the readout path adds no further noise. We show that the resulting signal-referred quantum noise spectral density is independent of the arm-cavity input and signal-extraction transmissivities at high frequencies, opening design freedom to mitigate technical constraints and radiation-pressure noise. We derive these results analytically, compare them with other internal squeezing and amplification schemes, and validate the full quantum-noise spectrum through numerical simulations. We also assess realistic implementations, including dissipation mechanisms and transverse mode mismatch introduced by the scheme, and find that 'mode healing' in the signal-extraction cavity can suppress mismatch losses. These results identify bidirectional internal squeezing as a possible upgrade path for gravitational-wave observatories such as LIGO, and the scheme may also benefit future observatories and other interferometry experiments.

Gravitational-wave observations are playing an instrumental role in understanding the population of binary compact objects in the Universe. These observations have begun to hint at the mass distribution of binary black holes (BBHs), with tentative evidence for features in the mass distribution beyond a simple power-law. Such features, hence, can be connected with different formation scenarios of BBHs and lead to important astrophysical conclusions. However, it is crucial to understand whether these features are truly astrophysical or connected with any unknown systematics. We show in this work that waveform modelling uncertainties can significantly distort inferred features in the BBH mass distribution, which can be more pronounced than the statistical uncertainty, even with the current generation detectors, which can peak close to the lower edge of the pair instability supernovae (PISN) mass gap, and also can impact the slope of the power-law distribution. So, in order to have a confirmed detection of astrophysical features in the BBH mass distribution and connecting them with BBH formation channels, it is important to consider waveform systematics in the astrophysical population analysis. We show the typical scaling of the systematic error and discuss a few avenues to mitigate this effect for robust measurements in the future.

We present Quokka#, a versatile, open-source Python library for quantum circuit analysis. Quokka# reduces various simulation, verification, and synthesis tasks to weighted model counting (#SAT). It supports universal quantum circuits and a wide variety of gates. Quokka# provides multiple encodings based on different algebraic bases and equivalence-checking methods, enabling key performance trade-offs. Moreover, the new version of Quokka# adds approximate equivalence checking, which is crucial in its synthesis algorithms, since it enables translation between arbitrary gate sets. Its synthesis engine is depth-optimal, making it well-suited to real-world quantum computing. This paper demonstrates the design, extensibility, and use of Quokka#.

We present a full 3D numerical evolution code to study neutron stars in massive-scalar-tensor theories. The code is embedded in the Einstein Toolkit framework and its implementation constitutes a modified version of the Baumgarte-Shapiro-Shibata-Nakamura formalism with an additional nonminimally coupled scalar field. The approach we follow preserves the standard hydrodynamic evolution for matter fields, allowing eventually for a straightforward inclusion of more microphysical effects and better flexibility. Using this code, we examine the gravitational collapse of rapidly rotating, scalarized neutron stars to a black hole by exploring the influence of the scalar field on the dynamical features of the process and on the gravitational-wave emission. We find that for the configurations studied in this work, there is an observational degeneracy in the tensorial gravitational-wave emission between collapsing scalarized stars and their counterparts in general relativity. However, this degeneracy can be broken through the emission of scalar radiation, which carries an energy of ~10^-3 M_sun c^2. This is orders of magnitude higher than the quadrupolar emission (~10^-7 M_sun c^2) and might be used as an observational probe of modified gravity. We also find that rapid rotation can enhance this signal, since fast rotating stars can sustain larger scalar field amplitudes.

The generalized quantum Stein's lemma provides an explicit expression for the optimal error exponent when distinguishing many independent and identically distributed (iid) copies of a given bipartite state from the set of separable bipartite states. Here we prove that this result is robust, in the sense that the iid assumption can be relaxed to almost-iid. In particular, our result shows that the original argument of Brandão and Plenio, which contains a logical gap, can be made rigorous. Our proof relies on a novel continuity bound for the relative entropy of entanglement with respect to the quantum Wasserstein distance. Combined with a recent insight that almost-iid states and their exact iid counterparts are asymptotically close in this distance, the bound implies that their relative entropies of entanglement coincide asymptotically.

Open quantum systems can host dark or subradiant states whose decay is highly suppressed. While these states have been extensively studied in the few-excitation regime, their impact on the many-body dynamics remains largely unexplored. Here, we study a spin chain subject to correlated dissipation on neighboring sites, which admits a single-particle dark state at zero momentum. We show that the single-particle dark state qualitatively alters the many-body dynamics at long times, and identify its distinct universal behavior. While the zero-momentum mode is dark at the single-particle level, it decays slowly as $1/\log t$ as it becomes dressed by other modes through a dissipation-induced nonlinearity. We demonstrate that the momentum distribution takes a universal scaling form in $k\sqrt{t}$, and the total density decays as $1/\sqrt{t}\log t$. Our results further elucidate the origin of the conflicting results in recent works. Finally, we corroborate the analytics with matrix product state simulations and show that the same universal behavior persists for soft-core interactions, underscoring the universality of the emergent dynamics.

We study the dynamical excitation of quasinormal modes (QNMs) during the plunge of a particle into a Schwarzschild black hole, building on the framework of Phys. Rev. D 113 (2026) 2, 024048 (Paper I). Investigating the high-frequency behavior of Leaver's QNM solutions, we obtain a more accurate and general prescription for their propagation. We confirm the existence of a new "characteristic radius" for QNM excitation, the bounce radius $r_*=0$, in agreement with recent literature. To its right, the QNM signal scatters off this point before reaching the observer; to its left, it propagates directly on the light-cone. Applying the formalism of Paper I to inspiralling particles, and using this refined prescription, we obtain a QNM signal that accurately reproduces the oscillatory component of the waveform after the bounce crossing, yielding an essentially complete first-principles description of the waveform from shortly after the signal peak. The dynamical QNM signal undergoes a transition as the particle crosses the bounce radius: from a quasi-resonant regime, where successive overtones are driven in counter-phase and interfere destructively, to a free-oscillator one, where they are in phase and the QNM sum converges rapidly. These results provide a clear physical interpretation of the collective QNM behavior during the plunge, and a firm theoretical foundation for accurate ringdown modelling.

We show analytically that the convergence of the QNM expansion of the retarded Green's function of the Schwarzschild spacetime is set by a singularity in the complex time plane. The singularity has a simple geometric origin: it is an example of a `bouncing singularity' in the language of AdS/CFT literature, caused by a null geodesic which bounces from the black hole singularity. Our work explains why the QNM convergence region at real times is bounded by null ray which scatters from the gravitational potential at a seemingly unremarkable point ($r_* = 0$ in the conventions of previous work) -- this ray is the same distance from the origin as the bouncing singularity in the relevant complex plane. The same set of singularities are responsible for an annular region of convergence for the Matsubara mode sum which describes the early time behaviour of the Schwarzschild Green's function for perturbations close to the horizon.

Entanglement and interference are among the most fundamental properties of quantum mechanics. In this work, we investigate the role and power of interference in the context of detecting entanglement. We do so from a computational complexity lens by proving that unentanglement gives no additional power to stoquastic Merlin-Arthur verification. For every polynomial number of provers $k=k(n)$, \[ \text{StoqMa}(k)=\text{StoqMa} . \] Conceptually, the proof separates the role of entanglement from the role of interference: once destructive interference is ruled out by stoquasticity, the product-state constraint can be absorbed into a polynomially larger one-witness stoquastic verification. The main analytic ingredient is a positive, value-based de Finetti theorem for separately symmetric extensions. If $M$ is an entrywise nonnegative positive semidefinite contraction on $A_1\otimes\cdots\otimes A_k$, then the nonnegative product value of $M$ is approximated to additive error $ε$ by the largest eigenvalue of \[ Π_R^{<k} (M_{A_{1,1}\cdots A_{k-1,1}A_k}\otimes I) Π_R^{<k}, \qquad R=O\!\left(\frac{k^2\sum_i\log\dim A_i}{ε^3}\right), \] where $Π_R^{<k}$ is the operator on $A_1^{\otimes R} \otimes \cdots \otimes A_{k-1}^{\otimes R} \otimes A_k$ projecting to the subspace $\mathrm{Sym}^R(A_1) \otimes \cdots \otimes \mathrm{Sym}^{R}(A_{k-1}) \otimes A_k$. The spectral relaxation is then realized as an actual one-witness stoquastic verifier. After replacing the uniform permutation averages in the symmetric projectors by inverse-polynomially close dyadic inverse-invariant averages. Consequently, \[ \text{StoqMa}(k)=\text{StoqMa}\subseteq\text{AM}\cap\text{PP}\subseteq\text{PSPACE} . \] The positive de Finetti theorem is isolated as a standalone technique and may be useful in other nonnegative tensor-optimization and stoquastic-verification settings.

Softmax and related normalized response functions are widely used in choice theory, machine learning, and cognitive science. In non-Boolean event structures with overlapping contexts, however, local normalization does not automatically yield a global probability weight. We show that imposing single-valuedness on shared atoms -- equivalently, no-disturbance or consistent connectedness -- collapses generalized softmax rules to coordinate parametrizations of the strictly positive part of the admissible-weight polytope. Any strictly positive admissible weight can be represented in this way, while boundary weights arise as limits. Exotic weights that exceed classical or quantum bounds are therefore properties of the event structure and the chosen weight, not of the normalizing link. The resulting hierarchy separates local normalization, cross-context gluing, Cauchy--Gleason linearity, and physical or cognitive realizability.

This is the first part of a series of papers proving the nonlinear stability of a one-parameter family of continuously self-similar $C^{1,α}$ naked singularity solutions, with $0<α\ll1$, to the spherically symmetric Einstein-scalar field equations. The stability holds for initial perturbations lying in a small open neighborhood of the data generating these naked singularity solutions, measured in a localized Hölder topology. These continuously self-similar naked singularity spacetimes were previously constructed by Christodoulou [D. Christodoulou, Examples of naked singularity formation in the gravitational collapse of a scalar field, Ann. of Math. 140 (1994), 607--653], who also proved their instability to black hole formation under sufficiently rough perturbations [D. Christodoulou, The instability of naked singularities in the gravitational collapse of a scalar field, Ann. of Math. 149 (1999), 183--217], thereby verifying weak cosmic censorship within a rough functional framework. In complete contrast, in this paper, we obtain the first nonlinear stability of these naked singularity spacetimes under general perturbations of the same regularity as the background. We rely on the linearized stability result established in the companion paper [J. Singh and W. Zheng, Nonlinear stability of continuously self-similar naked singularities for the Einstein--scalar field equations II: linearized stability]. Our result underscores the decisive role of the functional framework in formulating the Weak Cosmic Censorship conjecture.

Gravitational models with non-minimal couplings involving functions of the matter Lagrangian and curvature have become popular in recent decades. By coupling the matter Lagrangian directly to the gravitational Lagrangian, one hopes to construct theories that can explain dark energy or dark matter without introducing additional sources. When this matter Lagrangian describes a perfect fluid, some technicalities are involved in its variational formulation. We present a careful derivation of the gravitational field equations together with the complete set of fluid equations. The fluid's equations allow us to define thermodynamic quantities such as temperature, chemical potential and number density and thus allow us to understand the effects of the non-minimal couplings on these quantities. We demonstrate the non-equivalence of the Lagrangian formulations of Schutz and Brown for these types of models and provide a detailed interpretation of our results.

We investigate the model of spontaneous baryogenesis, considering two extensions to the background paradigm. Firstly, we introduce a non-minimal coupling between gravity and the inflaton, increasing the effective mass squared of the latter. In this way, the inflaton decays more likely into fermion-antifermion pairs during reheating, through baryon-number violating processes. Accordingly, we obtain an overall baryon asymmetry consistent with cosmological observations. Then, we consider a complex scalar spectator field interacting with the inflaton through a biquadratic coupling and non-minimally with gravity, and analyze the impact in terms of baryon asymmetry production. In this scenario too, the background model results significantly enhanced, but the predicted baryon-to-entropy ratio remains smaller than the experimental data.

The Boolean Satisfiability (SAT) problem is a canonical NP-complete problem and a natural candidate for quantum acceleration via search-based algorithms. In Grover-based quantum SAT solvers, the dominant computational cost stems from the construction of a reversible oracle that evaluates the Boolean formula, rendering the choice of SAT encoding crucial for overall quantum resource efficiency. Although SAT instances are conventionally expressed in Conjunctive Normal Form (CNF), such encodings typically translate into quantum circuits with significant qubit overhead and high non-Clifford gate complexity. In this work, we investigate an Exclusive-Sum-of-Products (ESOP)-based CNF (e-CNF) representation tailored for quantum SAT solving and analyze its impact on oracle construction. We derive tighter upper bounds on qubit requirements and Clifford+$T$ gate counts for Grover-based SAT solvers when e-CNF encodings are employed in place of standard CNF. In addition, we propose a scalable transformation from Boolean formulas to e-CNF and present a systematic procedure for interpreting e-CNF representations as reversible quantum circuits suitable for oracle implementation. Experimental evaluation on representative SAT benchmarks demonstrates that the proposed e-CNF-based approach yields substantial and consistent reductions in quantum resources, including qubit count, T-gate complexity, and circuit depth, when compared to CNF-based oracle constructions. These results establish e-CNF as an effective quantum-aware SAT encoding that significantly improves the practicality of oracle-based quantum SAT solving.

Quasinormal modes (QNMs) of black holes can exhibit avoided crossings (ACs), in which specific QNM frequencies approach each other while their amplitudes are enhanced and acquire nearly opposite phases, leading to characteristic interference. Resolving such closely spaced modes through black hole spectroscopy is observationally challenging. In this paper, we investigate the detectability of nearly degenerate QNMs in the presence of an AC within a Bayesian framework using three waveform models. We examine how the inference of the complex frequencies and amplitudes depends on the separation between the two QNM frequencies and on the choice of template waveform. We find that resolving the individual QNM frequencies is difficult even under optimistic conditions. On the other hand, collective waveform signatures associated with ACs may still be identified through complementary waveform descriptions, provided that the AC-related modes dominate the observed ringdown signal and contamination from more slowly damped modes is negligible or can be removed.

We present an efficient, nearly optimal quantum algorithm for solving linear matrix differential equations, with applications to the simulation of open quantum systems and beyond. For unitary or dissipative dynamics, the algorithm computes an entry of the solution matrix with query complexity $\widetilde{\mathcal{O}}(ν\mathcal{L} t/ε)$, where the constant $ν$ depends on the problem parameters, $\mathcal{L}$ involves a time integral of upper bounds on the norms of evolution operators, and $ε$ is the error. In particular, $ν\mathcal{L}$ is linear in $t$ for unitary dynamics and can be a constant for dissipative dynamics. Our result contrasts prior quantum approaches for differential equations that typically require exponential time for this problem due to the encoding in a quantum state, which can lead to exponentially small amplitudes. We demonstrate the utility of the algorithm through an end-to-end application, namely the simulation of dissipative dynamics for non-interacting fermions, which can be extended to other quantum and classical systems. We compare with classical algorithms and give evidence of polynomial quantum speedups for systems in a lattice, which become more pronounced for systems with long-range interactions and can be shown to be exponential in general. We also provide a lower bound of $Ω(ν\mathcal{L} t/ε)$ for unitary or dissipative dynamics that proves our algorithm is optimal up to logarithmic factors.

We present a quantum algorithm for solving algebraic Riccati equations, with applications to quantum-chemical random-phase approximation (RPA) and higher-order RPA theories. Our method block-encodes stabilizing Riccati solutions via Riesz projectors onto invariant subspaces of an associated non-normal matrix, implemented using contour-integral resolvents and quantum singular value transformations. Applied to $m$-particle, $m$-hole RPA, our algorithm yields a block-encoding of the amplitude solution and estimates the electronic correlation-energy density with it. Under localized-orbital sparsity assumptions, the end-to-end cost scales linearly with system size and polynomially with excitation rank $m$, suggesting an exponential advantage in $m$ over plausible classical local-correlation heuristics. More broadly, this work provides a framework for quantum algorithms for nonlinear matrix equations in quantum chemistry and opens a possible route toward developing quantum algorithms for coupled-cluster theory.

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

We investigate systems containing objects with negative mass (NMOs). In a system consisting of one object with positive mass and one NMO, a bound state exists even though the force exerted by the NMO on the object with positive mass is repulsive. Unlike a standard system consisting of two objects with positive mass, the gravitational waves emitted from this system exhibit a decrease in frequency and amplitude over time. We propose a model of the time evolution of the Ellis-Bronnikov wormhole, along with a formulation that eliminates the ghost that appears when constructing the Ellis-Bronnikov wormhole, a candidate for an NMO. Furthermore, numerical simulations are performed to obtain the optical appearance of such NMOs. The observed luminosity is also compared with the Schwarzschild black hole and with the Simpson-Visser wormhole, leading to clear differences in the photon ring substructure around the central object.

We find solutions to consistent truncations of supergravity where some real scalars are analytically extended to imaginary values, ensuring the metric remains real-valued. Among the solutions there are Lorentzian traversable wormholes connecting two asymptotically Anti-de Sitter spacetimes and flows that have a real metric also when uplifted to ten or eleven dimensions. We argue that the holographic duals are pseudo-Hermitian and $PT$-symmetric theories. Wormhole solutions also admit an interpretation as the low-energy theory of two stacks of branes and antibranes after tachyon condensation. The wormhole is then dual to an entangled state of two copies of the theory that lives on a stack of branes. We present some evidence by computing the mutual information between the theories at each boundary and by identifying the Goldstone bosons associated to the breaking of the two copies of Poincaré symmetry to their diagonal subgroup.

The possible interaction between the dark components of the Universe (dark matter and dark energy) stands as an attractive alternative to the standard $Λ$CDM cosmological model. In this work, we present a novel analysis of three sign-changeable interaction models whose coupling term $Q$ depends explicitly on the deceleration parameter $q$ and is proportional to different energy densities: dark matter, dark energy, and total energy density. To constrain these models, we combine strong gravitational lensing data on two complementary scales: a sample of early-type galaxies acting as lenses and the galaxy cluster Abell 1689. Our results show that the interaction strength $β$ depends on the choice of the coupling term $Q$, with all models yielding negative values of $β$, indicative of a dark interaction scenario. The $β$ values obtained in this work are significantly larger in magnitude than those previously reported using Type Ia supernovae, CMB, and BAO. The strong-lensing constraints indicate a transition to cosmic acceleration at earlier redshifts ($z_t \sim 1.8-2.1$) than that predicted by the $Λ$CDM model, while remaining consistent with cosmic chronometer measurements within the reconstructed confidence regions. Therefore, our study shows that strong gravitational lensing data provide an independent and competitive cosmological probe capable of testing interacting dark energy scenarios. The sensitivity of lensing observables to the expansion history enables access to complementary information about dark-sector dynamics beyond standard cosmological probes.

Quantum measurements can be incompatible, i.e., they can fail to be jointly measurable. Recently, a weaker notion of joint-measurability, called partial joint-measurability, was proposed by Masini et al. in [Quantum 8, 1574 (2024)]. In this work, we further generalize this notion to the setting where only a subset of the outcomes of each measurement is required to be jointly determined by classical variables. We provide two mathematical formulations of partial joint-measurability and show that, like full joint-measurability, it can be decided by solving a single semidefinite program. We prove that in the case of an untrusted measurement device, an adversary Eve, limited to classical side information, can perfectly guess the outcomes of the measurement device if and only if the set of measurements is partially jointly measurable. We derive analytical thresholds on the detection efficiency below which generic measurements become partially jointly measurable. Such bounds directly yield limits on the robustness of device-independent and semi-device-independent quantum cryptographic protocols against detection inefficiency. In particular, our results highlight the importance of a careful treatment of postselection in security analyses.

We consider the quantum creation of a closed universe within the Euclidean path-integral formalism. An analytical expression for the tunneling probability is derived, including both the exponential suppression and the exact Gaussian prefactor due to quadratic fluctuations around the instanton. The calculation is performed in a fixed-interval minisuperspace formulation, where the Hamiltonian constraint is imposed at the level of the classical instanton, while the full lapse integration is not included beyond the leading semiclassical approximation. The result provides a transparent and self-consistent semiclassical estimate of the nucleation rate, refining previous analyses with the inclusion of Gaussian fluctuations.

A reason is discussed (may be not the only one) for why we do not see any superposition of macroscopic states in the real world. Under the general assumption that quantum macrostates are statistical ensembles of microstates, it is shown that any superposition of macrostates is reduced in a very short time by the unitary dynamics of the ordinary Schrödinger equation, deducing the Born rule without having to postulate it. In more detail, the macroscopic and microscopic degrees of freedom are decoupled in the Schrödinger equation, yielding an effective stochastic equation for the macroscopic variables, with the ensemble average of the microscopic amplitudes that acts as a self-generated internal white noise. The stochastic equation is shown to be a reducing Itô equation if some general causality conditions are met, predicting a very quick collapse of any macroscopic superposition upon formation, with probabilities which satisfy the Born rule. In the context of the von Neumann measurement scheme, the relevance of the result is discussed as a simple dynamical solution of the measurement problem.

Dicke superradiance by an ensemble of quantum emitters produces a collective burst of radiation, but no entanglement in the mixed state of the emitters. We show that adding a local kinetic constraint between the emitters generates extensive mixed-state entanglement, while otherwise preserving all key features of Dicke superradiance. Specifically, for any local Boolean constraint, we analytically derive a lower bound for the emission rate which implies a peak intensity $\propto N^2$ and a peak time $\propto (\log N)/N$ with number of spins $N$. This effect enables the superradiantly accelerated preparation of entangled dark states. Hereby, Hilbert-space fragmentation of the Dicke ladder leads to an exponentially branching decay tree that generates a hierarchy of dark states. Importantly, these disconnected manifolds include exponentially many long-range entangled singlet dark states. The explored kinetic constraints and superradiant dynamics can be realized in neutral-atom arrays coupled to an optical cavity, and we suggest a simple and accessible witness to detect the predicted mixed-state entanglement in such experiments. Moreover, we show that entanglement generation is robust against atomic decay and collective dephasing, and should be observable under recently reported experimental conditions. Our results, thereby, offer a general framework and experimentally viable approach for the dissipative engineering of entangled dark states enhanced by superradiance.

Measurement is an important scientific activity. In most of science, including classical physics, is may be understood as a way of finding out about the physical world and representing the results numerically. No-go theorems show that measurement of quantum observables is not like that: the recorded outcome is typically created rather than revealed in a quantum measurement, in which case there is no objective fact about the observable's prior value. Other no-go theorems show that unitary quantum theory can generally neither explain nor even represent a unique recorded outcome, thereby threatening that outcome's objectivity. Methodological norms inherent in quantum physical practice nevertheless institute the objectivity, not only of unique recorded outcomes of quantum measurements, but also of non-quantum features of the world that physicists and other scientists take their models to represent.

This is the second part of a series of papers proving the nonlinear stability of a one-parameter family of continuous self-similar $C^{1,α}$ naked singularity solutions, with $0<α\ll1$, to the spherically symmetric Einstein-scalar field equations. These solutions were constructed by Christodoulou and are known to be unstable under sufficiently rough perturbations due to the blue-shift instability mechanism. In complete contrast to the previous instability results, we establish the linearized stability for those naked singularity spacetimes under perturbations of the same regularity as the background, revealing the central role of regularity in determining the strength of the blue-shift instability mechanism, and showing that it is not triggered at the regularity level of the background spacetime. The linear analysis carried out in this paper provides the foundation for the nonlinear stability result established in the companion paper [W. Zheng, Nonlinear stability of the continuous self-similar naked singularities for the Einstein-scalar field equations I: main results]. Together with that companion paper, this yields the nonlinear stability of these continuously self-similar naked singularities.

Quantum mechanics enables information-processing advantages even at the level of a single qubit. A paradigmatic example is the 2$\to$1 random access code (RAC), where a qubit outperforms a classical bit in retrieving encoded information. In the standard form, however, this quantum advantage is restricted to a single receiver, since decoding measurements inevitably destroy the encoded information. Contrary to this, we address how long the information encoded in a single qubit remains accessible even after multiple decoding, each with a quantum advantage. Introducing preparation distinguishability as an operational resource associated with the sender, we show that its interplay with measurement incompatibility on the receiver's side can mitigate measurement-induced disturbance, thereby enabling an arbitrarily long sequence of receivers to each retain a quantum advantage. Our results show that, even under repeated measurements, the information encoded in a qubit need not be entirely exhausted, revealing a stronger communication feature than previously recognised.

Order finding is the core subroutine of Shor's algorithm. On NISQ hardware, phase estimation output distributions are often distorted by noise, making correct order recovery difficult. We study recoverability in noisy order finding: given a measured precision-register distribution, when does standard classical post-processing still return the true order? We analyze 680 distributions from IBM quantum systems across problem instances and circuit settings. For each distribution, we apply continued-fraction post-processing with modular verification and define recoverability as whether the recovered order equals the true one. We characterize each distribution using four features: autocorrelation peak strength, normalized entropy, dominant verified mass fraction, and verified margin fraction. We evaluate these quantities using marginal feature comparisons, single-feature AUROC analysis, and multivariate tree-based classifiers. We use random-forest permutation importance to assess which quantities contribute distinct predictive information once the other features are known. To make classification behavior interpretable, we train a decision tree that exposes threshold rules for recoverable and non-recoverable distributions. We find that recoverability is strongly associated with residual comb-like structure in the measured distribution and the way verified probability mass is organized across candidate denominators. The dominant verified mass fraction is the strongest single-feature indicator of recoverability, and tree-based analysis shows it also provides the primary split in an interpretable threshold description. Some highly distorted distributions remain recoverable when one verified denominator dominates the post-processing mass, while some visibly structured distributions fail because classical post-processing favors an incorrect verified denominator.

We study mutually unbiased bases (MUBs) as structured finite initialization and adaptation families for variational quantum algorithms. The main theoretical result is that, in every dimension admitting a complete set of MUBs, the complete MUB ensemble maximizes isotropic Gaussian random-Hamiltonian width among all unions of d+1 orthonormal bases in C^d. Equivalently, within this basis-union class, it gives the smallest expected best-of-set minimum for random-Hamiltonian minimization. The proof represents each orthonormal basis as a regular-simplex Gaussian block and uses a centered-convex Gaussian correlation inequality to show that the independent-block case, realized by complete MUBs, is stochastically extremal. We also record a radial extension for Hamiltonians H=RG with R nonnegative and independent, and the unrestricted qubit case, where complete qubit MUBs are globally optimal among arbitrary six-state ensembles by a Bloch-sphere/octahedron mean-width argument. We then separate this coverage theorem from variational training dynamics. For diagonal QUBO costs, the MUB-family dependence of a fully matched construction collapses; for the canonical b=0 label it reduces to ordinary X-mixer QAOA. The empirical method is therefore adaptive MUB-XRot warm-start QAOA rather than canonical matched-mixer MUB-QAOA. In a cross-problem benchmark over MaxCut, weighted MaxCut, MIS, weighted MIS, and knapsack, adaptive MUB-XRot is non-worse than standard QAOA in 80.0% of 1500 paired cases, with win/tie/loss 829/371/300 and mean decoded-ratio improvement +0.1616. A separate QRAO MaxCut study shows that bit-flip MUB-family search reaches mean relaxed ratio 0.921 and improves over the X-variational baseline by +0.0608. The evidence is quality-oriented and incurs substantial runtime overhead; no quantum-advantage claim is made.

Dynamical quantum phase transitions (DQPTs) occur at times when a quantum state exhibits a nonanalytic change in its return probability. This can be viewed as the probability of collapsing the evolved state to the initial state by quantum measurement. However, the initial wave function usually has exponentially small amplitude in the late time evolved state. Here we perform statistical characterization for all the possible post-measurement states distributed according to the Born's rule, by sampling a one-dimensional quantum Ising chain after a quantum quench dynamics. The statistical ensemble can also be viewed as a mixed state when the time evolved state is subjected to maximally dephasing noise in a certain basis. We map the distribution to a statistical model and characterize its effective "energy" spectrum, and introduce the average dynamical free energy, establishing a framework for the statistical DQPTs. We show the recovering of DQPT under high-moment average and a delocalized level distribution following critical times. Through analytic continuation into the complex time plane, we demonstrate the vanishing of Yang-Lee-Fisher zeros and the emergent level crossing near critical times. Finally, we propose a measurement-based quantum computation protocol to simulate the unitary evolution via single-qubit measurements on a two-dimensional cluster state. Our results provide a way for experimentally investigating statistical DQPTs in quantum devices, shedding light on the structured circuit sampling with insights from DQPT and generalizing the understanding of mixed state due to decoherence beyond equilibrium.

Starting from a thorough analysis of the conjugate $\overline{H}$ of a complex Hilbert space $H$, including its significant importance regarding a representation of the tensor product of two complex Hilbert spaces and its impact to the theorem of Fréchet-Riesz over to a revisit of applications of nuclear and absolutely $p$-summing operators in algebraic quantum field theory (AQFT) in the sense of Araki, Haag and Kastler ($p=2$) and more recently in the framework of general probabilistic spaces ($p=1$), we will outline that Banach operator ideals in the sense of Pietsch, or equivalently tensor products of Banach spaces in the sense of Grothendieck are even lurking in the foundations and philosophy of quantum physics and quantum information theory. In particular, we concentrate on their importance in AQFT (Theorem 5.27). In doing so, we revisit the role of trace-class operators in quantum theory and construct the enveloping $\tup{C}^\adj$-algebra, corresponding to an arbitrarily given normed operator ideal (Proposition 5.3 and Theorem 5.5). Applications are presented, including a purely linear algebraic description of the quantum teleportation process, thereby showing a link to quantum information theory, also due to the emergence of the Hadamard-Walsh transform and the controlled NOT gate (Example 4.18). All Hilbert spaces discussed in this paper may be nonseparable (and hence infinite-dimensional).

The product formula, commonly known as Trotter decomposition, is a central tool for digital quantum simulation, whose performance depends critically on how the Hamiltonian is partitioned into tractable blocks. Standard decompositions typically rely on direct commutativity among Hamiltonian terms in a chosen operator representation, which can lead to large residual errors and deep circuits for complex, practically relevant many-body quantum systems. We address this fundamental bottleneck by introducing a new decomposition principle that goes beyond commutativity, grouping Hamiltonian terms into local three-site clusters according to the underlying SU(2) symmetry of the local dynamics. We show that three-site generators fall into at most four SU(2)-symmetry classes, each admitting an effective two-qubit SU(4) representation with exact and efficient implementations. By reducing the number of clusters, this decomposition principle substantially suppresses commutator-induced errors and circuit overhead while preserving underlying physical structures that commutativity-based decompositions may violate. We demonstrate the proposed method on several physically relevant spin-lattice models, where the reduced cluster structure can even realise the second-order product formula without doubling the circuit depth, as would be required by conventional decompositions. Numerical simulations of a Kagome Heisenberg model with triangular spin-chirality interactions show that the proposed method reduces both state infidelity and average spin-chirality bias by more than three orders of magnitude compared with conventional decompositions, while using substantially fewer gates. These results establish local symmetry as a flexible and practical design principle for product-formula simulation, opening a route to more accurate and hardware-efficient simulations of broader classes of many-body systems.

A fault-tolerant quantum computer is expected to require thousands of qubits. Trapped ion architectures provide a modular approach where the quantum register is divided into multiple subregisters connected by physically moving the corresponding ions. Transporting ions at scale comes with several challenges such as the need to connect thousands of control lines to an ion trap chip. Multiplexing the required control voltages from few input signals to multiple electrodes offers a solution to this wiring challenge. Here we demonstrate a quantum processing unit that combines a surface ion trap with a time multiplexer via a sample-and-hold technique that initially charges electrodes to fixed voltages and disconnects them during qubit operations. We characterize the unit's performance by measuring motional heating rates below one phonon per second in both open and closed switch configurations. We further characterize the sample and hold process and find that sampling intervals below 50 ms are sufficient to keep expected gate errors from decaying charges during the hold phase below $10^{-4}$. Our results indicate that the multiplexing scheme is compatible with high-fidelity operations.

We analytically prove the linear stability of non-Abelian black holes with quartic self-interactions. The background, constructed from the Wu-Yang magnetic monopole ansatz, is an exact black-hole solution carrying a non-Abelian magnetic charge $Q_{\rm NA}^2$ controlled by a single coupling parameter $χ$, and admits two distinct branches. The odd sector is always stable, while in the even sector the effective potential is positive for branch I and negative for branch II, establishing stability and instability, respectively. The instability of branch II is consistent with its connection to the perturbatively unstable Einstein-Yang-Mills Reissner-Nordström solution. Branch I remains linearly stable throughout the physical domain of $χ$ where the solutions are regular and free of naked singularities. Our results prove the existence of the first linearly stable asymptotically flat non-Abelian hairy black holes in a minimally coupled extension of four-dimensional Einstein-Yang-Mills theory.

We analyze the autonomous stabilization of remote entanglement by driving two distant qubits with the output of a correlated photon source. By treating the qubits as idealized entanglement detectors, we develop a general framework to quantify the maximum amount of entanglement that can be remotely stabilized in this way with a given photon source. We then apply this approach to evaluate the suitability of a single driven two-level system as a minimal resource for autonomous entanglement distribution schemes. While our analysis confirms the presence of distributable entanglement in the Mollow sidebands of a bare two-level system, we show that stabilizing close to maximally entangled states requires additional filter cavities that enhance the relevant correlated emission events compared to other processes. We identify optimized driving and cavity parameters and explain the achievable amount of entanglement in different regimes in terms of an effective two-mode squeezing model. These insights are particularly relevant for quantum networks based on photons or phonons in solid-state systems, where isolated spins, impurity centers, or other two-level defects are readily available, while alternative sources of correlated photons are difficult to realize.

Scaling up superconducting quantum processors remains a central challenge for realizing fault-tolerant quantum computation. Although distributed architectures based on optical photons offer a promising route to scalability, they require an efficient microwave-to-optical quantum transducer that operates at cryogenic temperatures. Existing approaches typically rely on strong optical pumping, which induces undesirable heating and degrades single-photon coherence. Here, we propose a microwave-to-optical quantum transducer based on double-resonant scattering from a single color center embedded in a diamond optomechanical resonator. We show that strong coupling between the color center and the optical cavity enables coherent conversion at extremely low pump powers on the order of 10 pW. The proposed device enables remote entanglement generation on the order of 1 kHz with a fidelity exceeding 0.9, demonstrating a viable pathway toward ultra-low-power, high-efficiency quantum transducers based on individual solid-state defects for future distributed superconducting quantum networks.

Dynamical quantum phase transitions in non-Hermitian systems pose fundamental challenges due to the intrinsic biorthogonality of their eigenstates. In this work, we extend a biorthogonal framework to investigate dynamical quantum phase transitions in non-Hermitian topological superconductors. Taking the non-Hermitian Kitaev chain as a prototypical model, we construct an associated-state formalism and reformulate the Loschmidt rate function, dynamical topological order parameter, and dynamical Fisher zeros. Within this framework, we find that the critical times at which dynamical quantum phase transitions occur differ from those based on the conventional self-normal approaches. We further analyze momentum-resolved subsystems at critical momenta and demonstrate the robustness of the biorthogonal framework. Our work highlights the essential role of biorthogonality in nonequilibrium dynamics and establishes a consistent theoretical framework for dynamical quantum phase transitions in non-Hermitian topological superconductors.

Density Functional Theory (DFT) is widely used for atomistic simulations. However, its reach stays limited due to several limitations such as lack of accurate exchange-correlation functional, requirement of costly O(N 3) diagonalization etc. Although quantum computing offers paths forward, including variational techniques, embedding strategies, and quantum linear solvers, the discussion remains scattered. Without shared terms or structure, evaluating progress in hybrid quantum-classical DFT efforts becomes challenging. To bring order, we introduce a three-axis scheme based on where the method connects into DFT, whether the quantum part boosts precision or cuts time, alongside intended device type: current noisy machines or future error-corrected ones. Sorting known approaches in this way shows why embedding frameworks fit modern tools better, while faster linear algebra waits for more advanced systems.

The Lieb-Schultz-Mattis (LSM) theorem and its higher-dimensional extensions forbid the existence of a unique, symmetric, and gapped ground state at fractional fillings in quantum many-body systems with a conserved particle number (or spin angular momentum) and the conventional translation symmetry of Euclidean lattices. In this work, we propose a generalization of the LSM theorem to quantum many-body systems on hyperbolic lattices, i.e., regular tessellations of two-dimensional negatively curved space. By leveraging concepts from hyperbolic band theory in a many-body setting, we adapt Oshikawa's flux-threading argument to periodic hyperbolic lattices with a non-Euclidean (Fuchsian) translation symmetry and compute a lower-bound to the ground-state degeneracy as a function of filling and lattice geometry. We explore the consequences of LSM constraints for gapped phases of hyperbolic quantum matter and suggest frustrated spin models on hyperbolic analogs of the square and triangular lattices as promising platforms for realizing symmetric spin liquids in hyperbolic space.

Classical transport equations with probabilistic initial conditions can be viewed as quantum systems. In a discrete version they are probabilistic automata. The time-local probabilistic information is encoded in a classical wave function. Its unitary evolution obeys a Schrödinger equation. Statistical observables are represented by operators which do not commute with the ones associated to classical observables. Examples are functions of the quantum energy or the quantum angular momentum. They are important conserved quantities. We construct a complex functional integral for the quantum system which describes the probabilistic classical transport equation. The characteristic features of quantum mechanics, as the superposition of wave functions, interference, the importance of phases, non-commuting operators or a unitary time evolution, are realized by probabilistic classical transport equations.

We consider a quantum system described by a quantum channel $Φ$ that is applied at every time step and study the time evolution of its information capacities. When $Φ$ is a GNS-symmetric channel (this includes Pauli channels, for example), we give explicit exponential convergence bounds for the classical and quantum capacities. These bounds are in terms of entropic properties of $Φ$. We further illustrate how these results help quantify the performance of active versus passive error-correction setups.

We propose a method for generating collective spin cat states in a cavity-coupled atomic ensemble by exploiting strong light-matter entanglement and anti-squeezing associated with the superradiant phase transition. We numerically and analytically demonstrate that the cat states can be heralded by photon-number measurement on the ground state of the Dicke model. The near-critical regime enhances both the cat-state size and the probability of obtaining larger photon-number outcomes, and outcomes with larger photon numbers yield even larger cat states. We also show that a thermodynamic-limit analysis clarifies the generation mechanism and connects it to a natural light-matter analogue of generalized photon subtraction for optical cat-state generation. These results suggest that exploiting criticality in strongly coupled light-matter systems could open new directions for matter-based many-body quantum technologies.

We experimentally demonstrate a universal, parameter-independent test for asymmetric source discrimination. The test allows us to discriminate faint sources well beyond the diffraction limit by exploiting spatial mode demultiplexing (SPADE) and single-photon detectors. Our test yields a rate of false negatives well below what can be achieved by diffraction-limited direct imaging. Our tabletop experimental setup is inspired by the problem of exoplanet detection, where one aims at detecting the presence of a faint source in the proximity of a brighter one. We present a complete theory, modelling arbitrary modal crosstalk, and collect data across a range of values for the source separations and intensity ratios. We show that SPADE retains an advantage over direct imaging in the relevant regime of small separations and low intensity ratios. Remarkably, we identify an experimentally accessible crosstalk threshold $C_{\mathrm{th}}\simeq 0.1$ below which the exponential rate of false negatives stays well below that of direct imaging. For example, for crosstalk of $10^{-2}$, SPADE needs up to one order of magnitude fewer photons than direct imaging to achieve the same error rate. These results demonstrate that SPADE offers an effective methodology for subdiffraction asymmetric hypothesis testing, under realistic imperfections and crosstalk, paving the way to photon-starved imaging tasks.

Variational neural network models have achieved remarkable success in solving ground-state problems of quantum many-body systems. However, addressing classical and quantum spin glasses remains challenging, as disorder and energy frustration give rise to an exponentially large number of local energy minima separated by high-energy barriers, hindering the efficiency of conventional Metropolis-based Monte Carlo methods. To bridge this gap, we introduce Deep Boltzmann Quantum States, a class of neural quantum states inspired by deep Boltzmann machines that inherit efficient block Gibbs sampling. We also propose two key advances in the training algorithm. Firstly, we combine natural-gradient updates with state-of-the-art stochastic optimizers. Secondly, we gradually tune the hardness of the problem Hamiltonian by interpolating from an easy to a hard regime, without the need to closely approximate the instantaneous adiabatic state at intermediate times. We match the exact solution or the best available estimate for several instances of classical and quantum Ising spin-glass models with infinite-range interactions and hundreds of spins. We also solve instances of the NP-hard Job Shop Scheduling Problem exceeding the current limitations of quantum annealing hardware. To summarize, deep neural architectures with efficient global update rules and trained within an annealing-like scheme, provide a powerful framework for solving real-world hard combinatorial optimization and for investigating disordered quantum many-body systems.

Decoherence is usually described as the loss of local quantum coherence after tracing over environmental degrees of freedom. This reduced description, however, hides the reservoir state that carries the lost coherence. Here we show that spin-boson decoherence can write a postselectable nonclassical imprint into a structured bosonic reservoir. We map zero- and finite-temperature reservoirs to one-dimensional chains, evolve the joint qubit-reservoir state with tensor-network dynamics, and reconstruct the Wigner function of a time-adaptive leading collective reservoir coordinate after transverse qubit readout. The conditioned mapped-reservoir coordinate develops Wigner negativity and interference fringes that are strongly suppressed in the unconditional reservoir state. A parameter sweep shows that the spectral exponent and temperature control the visibility of this conditional nonclassicality, the mapped-chain excitation transport, and the degree to which a single collective coordinate captures the imprint. These results provide a branch-resolved phase-space picture of decoherence: the reservoir is not only a sink for qubit coherence, but can carry a measurement-conditioned nonclassical state in a collective mapped coordinate.

The quantum Internet relies on the ability to distribute entangled quantum bits (ebits) between quantum memories at the end nodes, to perform applications like blind or distributed quantum computing that are impossible if end nodes are connected via a classical, i.e., non-quantum network. This need creates new challenges due to the fragile nature of entanglement, which decoheres over short timescales and cannot be amplified, buffered, or retransmitted. Two broad categories of approaches have been proposed in the scientific literature to realize such an entanglement distribution in a given path: one relying on a synchronous time-slotted model, and another one where intermediate nodes interact asynchronously. However, both of them implicitly assume a serial operation, where one ebit is established and made available to the application on end nodes before creating a new one. This is inefficient in long-range networks, with high transmission latencies, if the intermediate nodes have multiple memory qubits that could be used in parallel. To overcome this limitation, in this paper, we study the implications of multiplexing concurrent ebit requests on the same quantum, for both synchronous and asynchronous operation. Furthermore, for the latter, we define a novel distribution protocol, called HOPPER, where the intermediate nodes make autonomous and hop-by-hop decisions on the use of their local resources when establishing an ebit. With numerical simulations, we show that HOPPER is effective in handling multiple ebit requests in parallel, and it exhibits significantly better performance than a synchronous alternative in different scenarios.

We present the detailed analyses of five-dimensional loop quantum Kaluza-Klein cosmology based on the symmetric reduction of the connection formulation of the full theory. The previous results in a particular scenario are extended to more general cases. The effective scalar constraint for the geometric sector of the model is derived by the systematic semi-classical analysis in both the canonical and path-integral formulations, incorporating the quantum fluctuations as a subleading-order correction. The resulting effective scalar constraint not only exhibits the correct classical limit of the quantum system, but also serves as the basis for investigating the following three distinct effective scenarios through the incorporation of matter contributions: (i) vacuum, (ii) minimally coupling with a scalar field, and (iii) coupling with the dust. In all the three effective scenarios, the big bang and potential past big rip singularities in the classical model are naturally resolved by including the leading-order quantum correction of holonomies. Moreover, the visible universe undergoes a super-inflationary phase after overcoming the classical big bang singularity, during which the phenomenologically desired 55 e-folds can be achieved by appropriate initial conditions. In the case where the subleading-order quantum fluctuation term is included as a constant, the evolutions of the five-dimensional universe in all the three effective scenarios not only achieve sufficient inflation in the visible dimensions, but also exhibit re-collapse behaviors at certain large scales. Hence the cosmic inflation may originate from the interplay between compact extra dimensions and quantum geometric effects.

Quantum teleportation have a central role in quantum information science and allows transferring of an unknown quantum state through entanglement and classical communication. Unfortunately, the interaction with external and internal noise severely affects the quality of teleportation and poses limitations on practical applications of quantum communication networks. In this work, instead of conventional Bell teleportation, we introduce a Machine Learned adaptive protocol for optimizing multiple components of Quantum Teleportation in order to achieve higher fidelity in various noise environments. In order to demonstrate the performance of the proposed scheme, we study three different noise models, including bit-flip, amplitude damping, and depolarizing noise, both in case of single-qubit and two-qubit channels. As a result, we observe substantial improvement in the teleportation fidelity in comparison to the classical Bell-state teleportation protocol in certain noise conditions. Furthermore, the machine-learned protocol reveals a nontrivial strategy for compensation of decoherence and information losses. In addition, obtained results indicate the flexibility and reliability of the proposed framework for implementing various adaptive quantum communications while shedding light on possibilities of discovery of optimal quantum algorithms by means of automated approache

The Nakanishi Fujii Todo (NFT) algorithm, also known as Rotosolve, implements Sequential Minimal Optimization for Variational Quantum Eigensolvers (SMO-VQE) by exploiting the trigonometric dependence of the energy on individual circuit parameters. This enables analytical one-dimensional minimization using only a few , typically two, energy evaluations, but introduces bias in the estimated energy. Although performing additional measurements every few tens of iterations can mitigate bias accumulation, we find that such corrections often degrade optimization performance. In this paper, we analyze the origin and accumulation of bias during the SMO-VQE process. Specifically, we show that the bias can be accurately estimated without additional measurements. Furthermore, we find that bias correction destabilizes optimization along directions with small curvature, whereas the original biased estimator implicitly acts as a regularizer. Based on these insights, we propose a simple regularization method that implements error accumulation while maintaining unbiased energy estimation. The resulting algorithm consistently improves performance across different system sizes, circuit depths, target Hamiltonians, and measurement shots, with minimal hyperparameter tuning.

The dynamics of dissipative many-body quantum systems sometimes admit an emergent classical description in terms of a Markov chain even though the corresponding state space contains highly entangled states. In particular, a bad-cavity laser is a paradigm system whose dynamics can be formulated as a Markov chain in a two-dimensional state space spanned by collective angular momentum states. In this article, we investigate the connection between a dissipative phase transition that occurs in the subradiant regime of this system in the large atom number limit, and the properties of the underlying Markov chain. In one of the phases, the Markov chain approaches the detailed-balance condition with increasing atom number $N$ and hence becomes effectively time-reversible. This is caused by a collective atomic emission process that effectively reduces the Markov chain to one dimension. In the other phase, we find the emergence of time-asymmetric probability currents in the two-dimensional state space that signals a breakdown of detailed balance. This is accompanied by a macroscopic internal entropy production rate in the Markov chain that scales extensively with the atom number $N$. An observable consequence of the probability currents is a self-pulsing of the cavity light output in this phase, which can be detected as an anti-correlation dip in the intensity correlation function.

We show that for a CFT$_D$ on a flat open solid torus, the two point function in the Weyl frame is exactly paired with a finite geodesic lying entirely in the AdS$_{D+1}$ bulk interior. This relation is exact and requires neither large $N$, strong coupling, nor heavy operators. The standard boundary-anchored relation is a singular limit of the exact pair. For the free scalar, a mode expansion along $S^1$ generates an infinite tower of effective masses on $H_{D-1}$, whose intricate propagators resum exactly to the same simple higher-dimensional geodesic expression. Together with another exact pair between disjoint entanglement entropy and entanglement wedge cross-section found on the same open solid torus, this result points toward a broader exact-pair program in AdS/CFT.

We analyze PBH populations using state-of-the-art modeling of PBH binaries, deriving the strongest bounds on PBH abundance in the $0.6-100 M_\odot$ range from LIGO-Virgo-KAGRA O4a data and demonstrating sensitivity in the $10^{-4}-10^4 M_\odot$ range. The constraints are dominated by resolvable PBH mergers, while the associated gravitational wave background provides complementary but weaker limits. Allowing PBHs to account for a subset of the cataloged events slightly relaxes these bounds. However, a joint fit with astrophysical black holes shows no compelling evidence for a PBH contribution.

We develop a rigorous mathematical framework for quantum game theory applied to static 2x2 games, extending classical concepts to the quantum setting where players may employ arbitrary unitary operations (pure strategies) or probability measures over the continuous group SU(2) (mixed strategies). The Eisert-Wilkens-Lewenstein protocol is introduced as the standard implementation of quantum 2x2 games. We prove the existence of Nash equilibria for continuous quantum mixed strategies via a fixed-point argument, generalising the classical Nash theorem to the quantum case.

Traditional entanglement distillation follows a post-processing paradigm, a noisy quantum state, after full transmission through a noisy channel, is treated as a static resource to be purified via LOCC (local operations and classical communication). This work demonstrates a fundamentally different paradigm,pre-channel entanglement shaping (PES) -- actively engineering the system-environment coupling before or during channel transmission -- achieves a level of purification capability that is physically unattainable by any post-distillation protocol. We prove this separation using the framework of geometric entropy (quantum relative entropy to separable states). In post-distillation, the protocol can only select low-entropy sub-ensembles from a fixed mixed state, leaving the global geometric entropy unchanged or increased. In contrast, PES \textit{suppresses the rate of geometric entropy production} during channel evolution, resulting in a final state whose relative entropy of entanglement strictly exceeds the maximum achievable by post-distillation from the same channel. We provide explicit qubit channel examples, numerical simulations (with complete code in Appendix), and a geometric interpretation on the state manifold. Our result establishes pre-channel entanglement shaping as a distinct operational resource class, with immediate implications for quantum repeaters and entanglement-assisted communication. Very recently, Li \textit{et al.} experimentally demonstrated that preprocessing the entangling channel with optimally tailored local unitaries achieves entanglement fidelities unreachable by any postprocessing, revealing an intrinsic temporal asymmetry in entanglement distillation~\cite{Li2025}.

We study the timelike and null geodesic structure of a static, spherically symmetric black hole sourced by a Kalb--Ramond (KR) field coupled to nonlinear electrodynamics (NED). The geometry is characterized by the mass $M$, the magnetic monopole charge $q$, and the Lorentz-violating parameters $(γ,λ)$. Closed-form expressions are derived for the effective potential, as well as the specific energy and angular momentum of massive particles on circular orbits. We further analyze the photon sphere, black hole shadow, and the Lyapunov exponent associated with unstable null circular geodesics. The latter determines the eikonal quasinormal-mode frequencies through $ω_{\rm eik}=(\ell+1/2)\,Ω_c-i(n+1/2)\,|λ_L|$. The shadow radius is compared with the Event Horizon Telescope (EHT) observations of M87$^\ast$ and Sgr~A$^\ast$, allowing us to identify the viable region in the $(q,γ)$ parameter space. Finally, we compute the Hawking temperature, horizon area, and the Gray--Visser sparsity parameter. We demonstrate that the combined effects of the KR field and magnetic monopole charge increase the sparsity parameter from the Schwarzschild value $16π^3 \simeq 496$ to nearly $1.7\times10^3$. This indicates a significantly sparser Hawking cascade compared to the Schwarzschild case, while the photon ring remains consistent with the EHT $1σ$ observational bounds across most of the physically allowed parameter range.

We reveal the effect of the spacetime parameters on the accretion morphology formed through the BHL mechanism around a slowly rotating disformal Kerr black hole. Thus, we investigate the measurable signatures of these parameters on the hydrodynamical morphology and the timing behavior of the accreting flow. It is shown that even weak disformal deviations from the Kerr solution modify the shock-cone structure, enhance the density in the post-shock region, and produce coherent oscillations in the accretion rate. The Kerr model produces coherent peaks at 42.99 Hz and 68.13 Hz, and these frequencies are consistent with the high-frequency QPOs observed from the source GRS 1915+105. In the models where the deviations from the Kerr solution are weak, low-frequency QPOs are produced and found to be coherent. These frequencies also fall within the frequency range observed in Galactic black-hole binaries. On the other hand, the models with large deviations from Kerr can be used to explain observational results that are more irregular, broad-band, and contain multiple peaks. In addition, by using inverse-mass scaling in this work, the numerically calculated frequencies are also compared with observations of intermediate-mass and supermassive black holes. In particular, the disformal black-hole models are found to be consistent with the observational results obtained from the sources M82 X-1, NGC 5408 X-1, and RE J1034+396. This comparison also allows the possible black-hole mass range of observed sources to be inferred from the relation between simulated and observed frequencies. This makes BHL accretion in disformal Kerr geometry a powerful framework for connecting modified-gravity black-hole spacetimes with observable QPO phenomenology.

We develop a quantum landscape approach to characterize the long-time behavior of wave packet spreading in linear open quantum systems. Instead of treating diffusion, localization, and collapse of the wave packet as separate dynamical phenomena, we show that they originate from the symmetry structure of an underlying landscape in covariance space. The geometry of this landscape determines these distinct long time behaviors. Trapping potentials and bath fluctuations act as distinct symmetry-breaking perturbations, leading to noncommuting long-time limits and abrupt changes in the asymptotic wave-packet width. This geometric picture provides a unified origin for wave-packet diffusion, localization, and collapse in dissipative quantum dynamics.

Nitrogen-vacancy (NV) center ensembles provide a powerful platform for high-precision temperature sensing, with ongoing efforts to further enhance their measurement performance. In ensemble NV optically detected magnetic resonance (ODMR) spectra, commonly used Lorentzian and Voigt fitting models fail to accurately describe the spectral shape near the resonance frequency, leading to degraded precision in resonance-frequency determination and, consequently, temperature estimation. In this work, we analytically establish a new fitting method, termed dip-peak fitting, for extracting the resonance frequency from ensemble cw-ODMR spectra. Starting from a physical model that describes ensemble cw-ODMR spectra as a convolution of single-NV responses with distributed zero-field splitting and strain, we show that the spectral feature near resonance can be accurately approximated by a single Lorentzian function with a background term. The proposed fitting model reproduces the cw-ODMR spectrum around resonance more faithfully than conventional approaches, enabling faster and more accurate resonance-frequency determination under weaker microwave excitation. Experiments using fluorescent nanodiamond ensembles confirm the robustness and applicability of this method for high-precision temperature sensing.

Quantum programs often produce probability distributions rather than deterministic outputs, making verification inherently statistical and increasingly costly on real hardware. In practice, developers still frequently rely on testing with fixed shot budgets on simulators, which are simple but time-consuming and poorly suited to noisy backends. What is missing is a verification approach that is both statistically explicit and budget-aware. This paper formulates Bayesian sequential verification as a reference-based Bayesian hypothesis testing workflow in which priors are derived from explicit reference sources, such as finite-shot reference runs or ideal/statevector-based computation, and verification decisions are updated batch by batch as measurement evidence accumulates. This approach is evaluated in Qiskit on two complementary workloads: Bell-state and QAOA-MaxCut. Across both case studies, the results show that Bayesian sequential verification can substantially reduce measurement costs compared to fixed-budget baselines when the success probability of the program exceeds the target threshold. The findings position Bayesian sequential verification as a practical verification workflow for quantum programs. The approach provides a foundation for future quantum continuous-integration pipelines that require reliable, budget-aware pass/fail decisions and motivates validation on real quantum hardware.

We study the rotation of the polarization due to the gravitational field in the Kerr spacetime and the possibility of estimating the specific angular momentum that parameterizes this metric. Our approach is based on a geodesic interferometer, that is, a Mach-Zehnder interferometer whose arms are defined by null geodesics, and a single photon propagating within it. We show that the detection probability at the output ports of the interferometer is a function of two phase differences, one arising from the gravitational time delay and the other from the polarization rotation, both computed under the slow rotation and weak field approximations. Thereby, the interferometric visibility is a signature of two relativistic effects. Using the detection probability, we obtain an estimate for the specific angular momentum and characterize its uncertainty.

This work investigates the dynamical evolution of the universe within the framework of symmetric teleparallel $f(Q,\mathcal{T})$ gravity, where $Q$ is the non-metricity scalar and $\mathcal{T}$ is the trace of the energy-momentum tensor. We consider a spatially flat Friedmann-Robertson-Walker (FRW) metric and explore a specific functional form $f(Q,\mathcal{T}) = αQ + β\mathcal{T}$ to derive the gravitational field equations. To characterize the late-time cosmic acceleration, we utilize a model-independent approach by adopting a particular Hubble parameter $H(z)$ parametrization. The model parameters are constrained using the latest observational datasets, including the Hubble ($H(z)$) measurements and Pantheon+ samples. Our results indicate a transition from a decelerated to an accelerated expansion phase. We further examine the physical viability of the model through various cosmological diagnostics such as energy density, the equation of state parameter and thermodynamic properties. The analysis demonstrates that $f(Q,\mathcal{T})$ gravity provides a consistent alternative to the $Λ$CDM model in explaining the current accelerated expansion of the universe.

We construct a static spherically symmetric regular black hole with a Minkowski core, and a degenerate inner horizon with vanishing surface gravity. The spacetime contains a non-extremal outer horizon and exhibits two notable features. Firstly, in the large-mass regime with $r_+=2M$, the Kretschmann scalar becomes nearly independent of the ADM mass and is mainly controlled by the inner horizon radius $r_-$, so that the curvature of spacetime remains sub-Planckian everywhere by choosing $r_-$ appropriately. Secondly, the near inner horizon amplification is softened from exponential to power-law behavior. In particular, within the double-null shell and Ori models, the internal Misner-Sharp mass remains finite at late times and approaches $r_-/2$.

Charge-dependent scalarization of the Einstein-Euler-Heisenberg (EEH) black hole is carried out in the EEH-scalar theory by introducing an exponential scalar coupling with $α$ coupling constant to the Maxwell and nonlinear electrodynamic terms. The bald black hole (EEHBH) is described by mass $M$ and arbitrary magnetic charge $q$ and has a single horizon when choosing the action parameter $μ=0.3$. The spontaneous scalarization ($α^+$) of this black hole is available for charge $0<q< q_c=1.115$ and positive $α$, whereas its new scalarization ($α^-$) occurs for $q> q_c$ and negative $α$. The former case of $q=0.5$ implies infinite branches of scalarized EEHBHs but its fundamental branch ($n=0$) is stable against radial perturbations, while the latter cases of $q=2,20$ show two stable single branches of scalarized EEHBHs.

Dissipation remains a central obstacle to improving superconducting quantum circuits, yet the microscopic origins of loss in widely used materials platforms are not fully understood. Here, we report the observation of interface piezoelectricity-induced dissipation in superconducting qubits fabricated on high-resistivity silicon. Our devices use a transmon qubit with a shunt capacitor that simultaneously serves as an interdigital transducer embedded in a surface acoustic wave resonator. By tuning the qubit transition into resonance with discrete mechanical modes, we observe up to a factor-of-two reduction in qubit lifetime, consistent with energy exchange between the qubit and mechanical modes mediated by piezoelectric coupling at the aluminum-silicon interface. Our findings provide direct evidence for interface piezoelectricity as a distinct loss channel in superconducting qubits. Combined with multiphysics simulations, these findings suggest that interface piezoelectric loss can dominate over loss from two-level systems at sufficiently high frequencies.

Hybrid carbon nanotube-fullerene architectures provide a controllable platform for studying irreversibility and information flow in structured quantum environments. We analyze entropy generation in a system where paramagnetic endohedral fullerenes, such as N@C$_{60}$ and P@C$_{60}$, are encapsulated inside a suspended carbon nanotube (CNT) resonator, with selected multi-level fullerene spin states forming an effective qubit coupled to quantized CNT flexural modes. Building on prior work on fullerene-filled CNTs, spin-phonon control in suspended nanotubes, and phase-space propagators for damped driven oscillators, we develop a hybrid open-system model combining driven quantum Brownian motion of the CNT with an effective Jaynes-Cummings spin-vibrational interaction. The resonator dynamics are represented by a Wigner function whose evolution is written analytically in terms of the initial Wigner distribution and a Gaussian propagator. This phase-space description separates drive-induced displacement, diffusion, and damping, and connects these processes directly to entropy flow. The coupled spin-mechanical dynamics are embedded in a Lindblad master equation including mechanical damping, spin relaxation, pure dephasing, and thermally activated excitation. Within this framework we derive the entropy balance, identify entropy flux and non-negative entropy production, and examine how spin-vibrational hybridization redistributes irreversibility between coherent exchange and dissipative channels. We show that magnetic-gradient-enhanced spin-phonon coupling, resonant driving, and moderate thermal occupation produce crossovers between oscillator-dominated and spin-dominated entropy-production regimes. The framework provides a basis for using CNT-PEF hybrids as nanoscale platforms to study nonequilibrium quantum thermodynamics, decoherence, and information loss in vibrational environments.

The conjectures of Low and Natario--Tod, and Penrose's question on Arnold's Problem list ask if causality in spacetimes can be formulated in terms of linking of spheres of light rays in the manifold of all light rays. For $(2+1)$-dimensional spacetimes, this link happens in the manifold coverable by a solid torus $S^1\times \mathbb R^2$. This was solved positively by Chernov and Nemirovski, which raises the question of which link invariants can be used to study causality. Chernov, Martin and Petkova proved that Heegaard--Floer and Khovanov homology completely capture causality. Allen--Swenberg conjectured that the Jones polynomial, which is obtained as an alternating Euler characteristic from Khovanov homology, is also sufficient. But they constructed complicated examples of links $\mathrm{AS}(n)_{n=1}^{\infty}$ that suggest that the Alexander--Conway polynomial -- which is the Euler characteristic of Heegaard--Floer homology -- is not enough. The Links--Gould polynomial is a quantum invariant that specializes to the classical Alexander--Conway polynomial in two different ways and somewhat surprisingly inherits some of its characteristic classical features. We show that it distinguishes all the Allen-Swenberg links from the link of causally unrelated events and hence detects causality in all known examples where the Alexander--Conway polynomial is not sufficient. This suggests that it may completely capture causality. The work on the categorification of the Links--Gould Polynomial is an ongoing and hard problem, and it is not a subject of this paper. As a corollary, we also compute the Seifert genus of all Allen--Swenberg links.