arXiv subjects

Zhihao Ma

Publications and source records attributed to Zhihao Ma.

At least 19 recordsLinked to original sources

arXiv2026

Fitting RI-CLPM Is Not Enough: Diagnostic Sensitivity and Reporting Practices in Within-Person Longitudinal Research

The random-intercept cross-lagged panel model (RI-CLPM) is widely used to separate stable between-person differences from within-person dynamics. Yet fitting an RI-CLPM does not guarantee that the data can support meaningful within-person inference. We introduce the concept of RI-CLPM readiness, defined by measurement comparability and diagnostic sensitivity. Using Monte Carlo simulations with powRICLPM, we show that power to detect within-person cross-lagged effects depends jointly on reliability, ICC, number of waves, sample size, and target effect size; high ICC, modest reliability, and few waves can substantially reduce power even in large samples. We then review reporting practices in 186 empirical RI-CLPM applications. Many studies reported sample size, wave count, and reliability, but longitudinal measurement invariance, ICC or within-person variance, and sensitivity analyses were reported much less consistently. Null within-person paths were common but often interpreted without sufficient attention to diagnostic sensitivity. We argue that RI-CLPM results should be interpreted conditionally on data readiness and offer practical reporting recommendations.

stat.AP↗

arXiv2026

Thermal vapor quantum battery based on collective atomic spins

Quantum batteries harness non-classical resources, such as quantum coherence and entanglement, to surpass the performance limits of classical energy-storage devices. Here we realize a room-temperature quantum battery based on a collective atomic spin ensemble in a thermal alkali-metal vapor, containing approximately $10^{12}$ $^{87}$Rb atoms with coherence times exceeding 110 ms. We operationally determine the battery capacity by directly measuring the extremal internal energies accessible under unitary evolution. This tomography-free protocol agrees closely with the conventional state-based definition and verifies the decomposition of capacity into coherent and incoherent contributions. We further show that quantum coherence can substantially enhance the storage capability independently of level populations, and experimentally establish quantitative relations linking battery capacity to von Neumann, Tsallis and linear entropies. By introducing a controlled dephasing channel with a magnetic-field gradient, we observe a monotonic reduction of capacity with coherence loss and track the corresponding evolution of the entropy-capacity relations. Our results identify thermal atomic spin ensembles as a scalable platform for quantum batteries and connect macroscopic quantum energy storage with operational quantum thermodynamics.

quant-ph↗

arXiv2026

Noise is not always detrimental: the capacity of quantum batteries is enhanced in black holes

Quantum battery capacity, as a critical metric for quantifying energy storage and release in quantum systems, exhibits complex behaviors in curved spacetime and noisy environments. This study focuses on bipartite mixed state, aiming to explore the modulation of quantum battery capacity by Hawking radiation and environmental noise. We find a counterintuitive phenomenon that Hawking radiation can enhance battery capacity, exerting a positive influence on energy storage, a result that stands in stark contrast to the detrimental effects typically associated with entanglement and coherence. When a quantum battery is simultaneously subjected to environmental noise and Hawking radiation, its capacity generally degrades, with the extent of degradation depending on the type of noise. The charging and discharging behaviors largely follow the same patterns observed in the noiseless scenario; however, under a bit flip channel with strong noise intensity, the charging-discharging pattern reverses. In the extreme case of maximum noise intensity, the capacity of the quantum battery under depolarizing noise tends to zero. The underlying physical mechanism lies in the fact that the bit flip channel disrupts the original population distribution of energy levels, thereby altering the average energy of the system and establishing a perturbative environment for bidirectional energy exchange. This differs fundamentally from the phase flip channel. These findings offer a new perspective for the theory of quantum batteries in noninertial reference frames.

quant-ph↗

arXiv2026

Simultaneous Detection of High-Dimensional Entanglement for Two Unknown Quantum States

The state overlap, quantified via $\tr[\rho \sigma]$, is a metric widely used to assess the closeness between two quantum states $\rho$ and $\sigma$. Although global state overlap alone does not directly capture entanglement properties, we uncover that incorporating local state overlaps provide profound insights into the entanglement characteristics of quantum states. To be precise, the ratio of global to local state overlaps provides a lower bound on the Schmidt number, which is usually used for quantifying high-dimensional entanglement. Unlike conventional methods for detecting entanglement, the approach here can simultaneously reveal entanglement information for two unknown quantum states. Moreover, state overlap can be efficiently determined through local randomized measurement methods, which ensures the experimental feasibility of our approach. In a special case, our criterion reduces to an entanglement criterion that is more powerful than the two criteria used most in experiment--the purity criterion and the fidelity-based criterion and also outperform the $p_3$-PPT method in specific instances. Our findings highlight a promising direction for advancements in entanglement detection experiments.

quant-ph↗

arXiv2026

Digital exclusion among middle-aged and older adults in China: age-period-cohort evidence from three national surveys, 2011-2022

Amid China's ageing and digital shift, digital exclusion among older adults poses an urgent challenge. To unpack this phenomenon, this study disentangles age, period, and cohort effects on digital exclusion among middle-aged and older Chinese adults. Using three nationally representative surveys (CHARLS 2011-2020, CFPS 2010-2022, and CGSS 2010-2021), we fitted hierarchical age-period-cohort (HAPC) models weighted by cross-sectional survey weights and stabilized inverse probability weights for item response. We further assessed heterogeneity by urban-rural residence, region, multimorbidity, and cognitive risk, and evaluated robustness with APC bounding analyses. Across datasets, digital exclusion increased with age and displayed mild non-linearity, with a small midlife easing followed by a sharper rise at older ages. Period effects declined over the 2010s and early 2020s, although the pace of improvement differed across survey windows. Cohort deviations were present but less consistent than age and period patterns, with an additional excess risk concentrated among cohorts born in the 1950s. Rural and western residents, as well as adults with multimorbidity or cognitive risk, remained consistently more excluded. Over the study period, the urban-rural divide showed no evidence of narrowing, whereas the cognitive-risk gap widened. These findings highlight digital inclusion as a vital pathway for older adults to remain integral participants in an evolving digital society.

stat.AP↗

arXiv2026

The Fragility of Global Comparisons of Perceived Scientist Trustworthiness: Evidence from Measurement Alignment across 68 Countries/Regions

Cologna et al. (2025) compared perceived scientist trustworthiness across 68 countries/regions and examined its associations with individual- and country-level factors. While the authors reported that the scale did not satisfy metric and scalar measurement invariance, their subsequent cross-national/regional comparisons and regressions were nonetheless conducted using weighted means of observed item scores, implicitly assuming cross-country/region comparability at the observed-score level. Using the publicly shared dataset, we re-evaluated these conclusions by systematically applying measurement alignment under four analytical paths: pooled-sample versus country/region-specific confirmatory factor analysis (CFA), each estimated with and without weights. Across all specifications, cross-national/regional CFA supported configural and metric invariance but failed to establish scalar or strict invariance. Importantly, under the analytical path most closely corresponding to the original study (pooled CFA with weights), only the competence and openness factors yielded admissible aligned solutions within the four-factor model. Using aligned latent scores for these two dimensions, country/region rankings changed for 62 of the 68 countries/regions. Substantive conclusions also differed: associations between perceived scientist trustworthiness and science-related populist attitudes or social dominance orientation were near zero or non-robust, whereas attitudes toward science remained strongly and positively related. Taken together, these findings demonstrate that cross-national/regional comparisons based on observed-score averages may be misleading when measurement equivalence is not established, and that latent-variable approaches such as alignment provide a more defensible basis for international inference.

physics.soc-ph↗

arXiv2026

The spatiotemporal Born rule is quasiprobabilistic

Contrary to general relativity, quantum theory treats space and time in fundamentally different ways. In particular, while joint probabilities associated with spacelike separated measurements are defined in terms of the Born rule, joint probabilities associated with measurements performed in sequence are defined in terms of the state-update rule. In this work, we show that one obtains a more unified perspective of space and time in quantum theory by embracing a quasiprobabilistic description of sequential measurements. More precisely, we show that there exists a unique \emph{pseudo}-density operator encoding canonical quasiprobabilities associated with sequential measurements in precisely the same manner that a density operator encodes joint probabilities associated with spacelike separated measurements, thus providing a natural extension of the Born rule into the temporal domain. As an application, we show how such a spatiotemporal Born rule combined in conjunction with a quantum Bayes' rule yields an operational notion of time-reversal symmetry for sequential measurements on an \emph{open} quantum system.

quant-ph↗

arXiv2026

Machine learning-aided direct estimation of coherence and entanglement for unknown states

Quantum coherence and entanglement are fundamental resources in quantum technologies, yet their efficient estimation for unknown states by employing minimal resources in experimental settings remains challenging, particularly in high-dimensional systems. We present a machine learning approach based on support vector regression (SVR) that directly estimates the coherence measures and the geometric measure of quantum entanglement using minimal experimental resources. Our method requires only the diagonal entries of the density matrix, along with the traces of the squared and cubed density matrices for quantum coherence, and additionally along with the traces of the squared and cubed reduced density matrix for estimating quantum entanglement. These quantities can be obtained through random measurements or a hybrid quantum-classical framework. This approach significantly reduces the resource overhead compared to quantum state tomography while maintaining high accuracy. {Furthermore, the support vector quantile regression (SVQR) with pinball loss is employed to prevent SVR overestimation. This model not only ensures that over 95\% of predictions are conservative lower bounds in most cases, but also maintains this lower-bound reliability for over 93\% of predictions, despite 2\% perturbations in the input features.} The proposed technique provides a practical and scalable tool for characterizing quantum resources across computation, communication, and metrology applications.

quant-ph↗

arXiv2025

On small perturbations of coherent information

Quantum capacity quantifies the amount of quantum information that can be transmitted by a quantum channel with an arbitrary small probability of error. Mathematically, the quantum capacity is given by an asymptotic formula involving the one-shot quantum capacity of the associated channel, which, due to purely quantum effects such as superadditivity of one-shot quantum capacity, is rarely computable. The one-shot quantum capacity is mathematically characterized in terms of optimizing an entropic quantity referred to as coherent information over all possible input states of a channel, the computation of which also tends to be intractable due to the difficulty of optimizing the coherent information. In this work, we develop perturbative methods for analyzing the behavior of coherent information of a quantum channel with respect to small perturbations of the input state. By doing so, we are able to derive three general criteria for determining whether an input state yields suboptimal coherent information. We then show how our criteria yield sufficient conditions for superadditivity of one-shot quantum capacity, and also for detecting a positive gap between one-shot private capacity and one-shot quantum capacity. The utility of our criteria is illustrated through examples, which yield new results regarding the one-shot quantum capacity of qubit depolarizing channels, Pauli channels and dephrasure channels.

quant-ph↗

arXiv2025

Psychometric Comparability of LLM-Based Digital Twins

Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain. We propose a construct validity framework spanning construct representation and the nomothetic span, benchmarking models against human gold standards. Across studies, digital twins achieved high aggregate-level accuracy and profile correlations, but showed attenuated item-level correlations. In word association tests, LLM networks exhibited humanlike small-world structure and theory-consistent communities, yet diverged lexically and in local structure. In decision-making and contextualized tasks, they under-reproduced heuristic biases, demonstrating normative rationality, compressed variance, and limited temporal sensitivity. Feature-rich and trait relevant conditioning improved Big Five personality prediction and nomothetic-span alignment, but network invariance remained limited, with partial configural solutions and persistent loading differences. In cross-language free-text tasks in English and Chinese, feature-rich digital twins better approximated construct-level narrative content, but linguistic and idiographic differences persisted. These findings clarify that digital twins are most useful within validated boundaries, where the construct, task and level of inference align with evidence from human data.

cs.CY↗

arXiv2025

Semi-distributed Cross-modal Air-Ground Relative Localization

Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems with the same sensor configuration, which are tightly coupled with the state estimation of all robots, limiting both flexibility and accuracy. To this end, we fully leverage the high capacity of Unmanned Ground Vehicle (UGV) to integrate multiple sensors, enabling a semi-distributed cross-modal air-ground relative localization framework. In this work, both the UGV and the Unmanned Aerial Vehicle (UAV) independently perform SLAM while extracting deep learning-based keypoints and global descriptors, which decouples the relative localization from the state estimation of all agents. The UGV employs a local Bundle Adjustment (BA) with LiDAR, camera, and an IMU to rapidly obtain accurate relative pose estimates. The BA process adopts sparse keypoint optimization and is divided into two stages: First, optimizing camera poses interpolated from LiDAR-Inertial Odometry (LIO), followed by estimating the relative camera poses between the UGV and UAV. Additionally, we implement an incremental loop closure detection algorithm using deep learning-based descriptors to maintain and retrieve keyframes efficiently. Experimental results demonstrate that our method achieves outstanding performance in both accuracy and efficiency. Unlike traditional multi-robot SLAM approaches that transmit images or point clouds, our method only transmits keypoint pixels and their descriptors, effectively constraining the communication bandwidth under 0.3 Mbps. Codes and data will be publicly available on https://github.com/Ascbpiac/cross-model-relative-localization.git.

cs.RO↗

arXiv2025

Static and dynamic coherence fraction in the Bernstein-Vazirani algorithm

Quantum entanglement and coherence are crucial resources in quantum information theory. In some scenarios, however, it is not necessary to directly estimate entanglement or coherence measures to quantify the capabilities of a state in quantum information processing. Instead, fully entangled fraction and coherence fraction are two alternatives for entanglement and coherence in specific quantum tasks. Here, we establish a link between the coherence fraction and the Bernstein-Vazirani algorithm, which has several potential applications including cryptography and database search. We show that the success probability of the generalized Bernstein-Vazirani algorithm depends only on the coherence fraction of the initial state rather than its entanglement or coherence. Moreover, we discuss the coherence fraction dynamics and establish a relation between the operator's coherence fraction and the algorithm's success probability. Our findings highlight how quantum coherence fraction influences the efficiency of quantum algorithms.

quant-ph↗

arXiv2025

Decoding Quantum Search Advantage: The Critical Role of State Properties in Random Walks

Quantum algorithms have demonstrated provable speedups over classical counterparts, yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge. In this work, we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms. We propose three distinct variants of quantum random-walk search algorithms and derive exact analytical expressions for their success probabilities. These probabilities are fundamentally determined by specific initial state properties: the coherence fraction governs the first algorithm's performance, while entanglement and coherence dominate the outcomes of the second and third algorithms, respectively. We show that increased coherence fraction enhances success probability, but greater entanglement and coherence reduce it in the latter two cases. These findings reveal fundamental insights into harnessing quantum properties for advantage and guide algorithm design. Our searches achieve Grover-like speedups and show significant potential for quantum-enhanced machine learning.

quant-ph↗

arXiv2025

Coherence Fraction in Grover Search Algorithm

The question of which resources drive the advantages in quantum algorithms has long been a fundamental challenge. While entanglement and coherence are critical to many quantum algorithms, our results indicate that they do not fully explain the quantum advantage achieved by the Grover search algorithm. By introducing a generalized Grover search algorithm, we demonstrate that the success probability depends not only on the querying number of oracles but also on the coherence fraction, which quantifies the fidelity between an arbitrary initial quantum state and the equal superposition state. Additionally, we explore the role of the coherence fraction in the quantum minimization algorithm, which offers a framework for solving complex problems in quantum machine learning. These findings offer insights into the origins of quantum advantage and open pathways for the development of new quantum algorithms.

quant-ph↗

arXiv2025

Unified Construction of Genuine Multipartite Entanglement Measures Based on Geometric Mean and its Applications

Genuine multipartite entanglement (GME) is an important resource in quantum information processing. We systematically study the measures of GME based on the geometric mean of bi-partition entanglements and present a unified construction of GME measures, which gives rise to the widely used GME measures including GME concurrence, the convex-roof extended negativity of GME, the geometric measure of entanglement of GME. Our GME measures satisfy the desirable conditions such as scalability and smoothness. Moreover, we provide fidelity-based analytical lower bounds for our GME measures. Our bounds are tight and can be estimated experiment friendly without requiring quantum state tomography. Furthermore, we apply our results to study the dynamics of GME. We identify an initial condition that influences the sudden death of genuine quadripartite entanglement under individual non-Markovian processes. The GME of Dirac particles with Hawking radiation in the background of a Schwarzschild black hole is also investigated.

quant-ph↗

arXiv2025

Experimental Characterization of Quantumness Using the Uncertainty Principle, Coherence, and Nonlocality

Heisenberg's uncertainty principle, coherence and Bell nonlocality have been individually examined through many experiments. In this Letter, we systematically characterize all of this quantumness in a unified manner. We first construct universal uncertainty relations to reveal intrinsic features of incompatible measurements, which include all the state-independent uncertainties as special cases. We further extend to witness both quantum coherence and Bell nonlocality. We finally perform experiments with unified two-photon states, and validate the uncertainty principle, coherence and Bell nonlocality within the experimental error. Our methods for witnessing quantumness are valuable in characterizing quantum correlations in quantum information processing.

quant-ph↗

arXiv2025

A coherence monotone from Kirkwood-Dirac nonclassicality with respect to mutually unbiased bases

The Kirkwood-Dirac distribution, serving as an informationally complete representation of a quantum state, has recently garnered increasing attention. We investigate the Kirkwood-Dirac classicality with respect to mutually unbiased bases. For prime dimensional Hilbert spaces, {we demonstrate that a quantum state exhibits Kirkwood-Dirac classicality for two distinct sets of mutually unbiased bases $(A,B)$ and $(A,B')$ if and only if it is incoherent with respect to $A$}. We subsequently introduce a coherence monotone based on Kirkwood-Dirac nonclassicality with respect to mutually unbiased bases. Additionally, we establish that this coherence monotone can be expressed through weak values, suggesting that quantum coherence can be utilized to detect anomalous weak values.

quant-ph↗

arXiv2025

Super-additivity of quantum capacity in simple channels

The super-additivity of quantum channel capacity is an important feature of quantum information theory different from classical theory, which has been attracting attention. Recently a special channel called ``platypus channel'' exhibits super-additive quantum capacity when combined with qudit erasure channels. Here we consider the ``generalized platypus channel'', prove that it has computable channel capacities, such as both private and classical capacity equal to $1$, and in particular, the generalized platypus channel still displays the super-additivity of quantum capacity when combined with qudit erasure channels and multilevel amplitude damping channels respectively.

quant-ph↗