arXiv subjects

Zheng Yan

Publications and source records attributed to Zheng Yan.

At least 19 recordsLinked to original sources

arXiv2026

Observable-Based Learning of Effective Entanglement Hamiltonians

Determining the entanglement Hamiltonian (EH) of an interacting quantum many-body system remains a fundamental challenge beyond special analytically tractable cases. Here we develop an observable-matching framework for learning effective EHs, which we demonstrate using response estimates from quantum Monte Carlo. As applications of this framework, we first calibrate the overall prefactor of the lattice Bisognano--Wichmann (LBW) construction using equal-time correlations and then learn spatially resolved couplings beyond its prescribed profile, without assuming a specific functional form for their spatial dependence. Trained solely on local correlations, the learned EHs reproduce benchmarked von Neumann and second Rényi entropies in $1$D and second Rényi entropies in $2$D. For a strong-bond cut associated with special surface criticality at the $(2+1)$D $O(3)$ Wilson--Fisher critical point, free-form learning outperforms the calibrated LBW ansatz in predicting the second Rényi entropy and reveals spatially nonuniform coupling renormalizations. Importantly, the learned EH also provides access to the von Neumann entropy in this setting, revealing a negative subleading correction and extending previous Rényi entropy findings to the von Neumann case. Directly evaluating the von Neumann entropy in such $(2+1)$D interacting quantum many-body systems remains challenging for conventional numerical methods. These results establish observable-based learning as a route to constructing effective entanglement Hamiltonians beyond prescribed spatial profiles in higher-dimensional interacting systems.

cond-mat.str-el↗

arXiv2026

Finite-size scaling theory of Rényi thermal entropy for second-order, first-order, and weakly first-order quantum phase transitions

Establishing the nature of a quantum phase transition in finite-size simulations---whether continuous, first-order, or weakly first-order---is a fundamental challenge in quantum many-body computation. The weakly first-order transition is particularly elusive: its anomalously large correlation length invariably mimics a continuous critical point at accessible system sizes, and no unified finite-size scaling theory has existed to distinguish these scenarios under realistic computational constraints. In this work, we address this problem by introducing a finite-size scaling framework based on the Rényi thermal entropy (RTE) and its derivative (DRTE). We derive the finite-size scaling forms for the RTE and DRTE at continuous, first-order, and weakly first-order quantum phase transitions, and show that the DRTE isolates the universal contribution of the free energy and strengthens the characteristics of various phase transitions in finite sizes. Using quantum Monte Carlo simulations, we demonstrate accurate data collapse and extraction of critical exponents at (2+1)-dimensional O($N$) critical points. Most importantly, the DRTE provides a definitive signature of weakly first-order transitions at visitable sizes---a zero-crossing and a double-peak structure---which we observe in the debated deconfined quantum criticality candidates such as the $J$--$Q$ and SU(8) $J_1$--$J_2$ models. Our approach offers a general, order-parameter-agnostic, and numerically efficient diagnostic tool, resolving long-standing ambiguities between continuous and weakly first-order scenarios in the finite size simulations.

cond-mat.str-el↗

arXiv2026

ATI-OPD: Adaptive Teacher Intervention for Stable Long-Sequence On-Policy Distillation

On-policy distillation (OPD) trains a student on its own responses using token-level feedback from a teacher. As responses grow longer, the student's prefix can depart from the teacher's own reasoning, weakening its feedback on later tokens. Updates based on this feedback can reinforce the same deviations in later responses and destabilize training. Prior approaches such as length control and loss reweighting limit learning from problematic responses but leave their prefixes unchanged. Fixed cutoffs can also discard later reasoning. We propose \textbf{Adaptive Teacher Intervention for On-Policy Distillation (ATI-OPD)} to improve subsequent teacher guidance by revising the student's reasoning prefix. ATI-OPD replaces the earliest high-disagreement segment with a short teacher segment, then lets the student resume its own reasoning from the revised text. To handle renewed disagreement, it excludes persistently divergent suffixes from the distillation loss while retaining useful student continuation. Training diagnostics show less teacher-student disagreement, fewer gradient spikes, and fewer extremely long responses. Across five mathematical reasoning benchmarks and two teacher-student pairs, ATI-OPD improves average student-only accuracy over standard OPD.

cs.CL↗

arXiv2026

Microscopic Bipartition and Macroscopic Symmetry in a Rényi Entanglement Ensemble

Universal information extracted from entanglement at a critical point is commonly associated with the long-distance properties of the bulk state, while microscopic details of a lattice bipartition are expected to be washed out under coarse graining. Counterintuitively, we show directly that such microscopic information can survive in the statistical ensemble that determines the second Rényi entropy and obviously change the physics there. Using quantum Monte Carlo simulations of the square-lattice $J$-$Q_3$ model at the Néel--valence-bond-solid (VBS) transition, we measure magnetic and dimer order parameters in the Rényi-2 ensemble. For a standard axis-aligned bipartition, the VBS component perpendicular to the entanglement boundary remains finite in the thermodynamic extrapolation and is inequivalent to the parallel component, while the Néel order vanishes. For a $45^\circ$ tilted bipartition, in contrast, the two VBS components remain equivalent within numerical accuracy. We interpret this contrast in terms of the Rényi twist defect: the two bipartitions have the same bulk theory but realize different microscopic defect symmetries. Our results demonstrate that lattice information invisible to the bulk continuum theory alone can remain active in the long-distance Rényi ensemble, providing a microscopic explanation for the strong bipartition dependence previously observed in entanglement studies of deconfined criticality.

quant-ph↗

arXiv2026

Scalable Quantum Monte Carlo for Shannon-Rényi Entropy: An Experiment-visitable Probe of Quantum Phases and Criticality

The Shannon-Rényi (SR) entropy characterizes the probability distribution of many-body wave functions in a chosen basis and is directly accessible from single-shot measurement snapshots in cold-atom experiments, yet its theoretical evaluation in interacting systems carries exponential cost. Here we construct a field-theoretic spacetime manifold for the SR entropy in which $q$ replicas are glued at the imaginary-time boundaries, recasting it as a ratio of generalized partition functions amenable to large-scale quantum Monte Carlo simulation. On this basis we develop a reweight-annealing framework with two complementary strategies: a virtual-parameter scheme interpolating between unglued and fully glued limits via binomial-weighted partial gluing, and a real-parameter scheme using a physical coupling constant as the annealing variable within stochastic series expansion. Both reduce the computational cost from exponential to polynomial. Most importantly, we establish for the first time a finite-size scaling theory for the SR entropy near a quantum critical point, $S_q=a(g)L^2+S_q^0\left((g-g_c)L^{1/ν}\right)$; data collapse of its first derivative yields the correlation-length exponent $ν$ for both the O(3) and Ising universality classes, realized in the spin-$\frac{1}{2}$ columnar dimerized Heisenberg model and the bilayer Heisenberg-Ising model. The same simulations extract the Goldstone-mode count $N_G=2$ and the $Z_2$ ground-state degeneracy $\ln 2$ from subleading terms. These results elevate the SR entropy to a standalone, order-parameter-free diagnostic of quantum criticality with the same scaling status as the entanglement entropy, and close the loop between theory and quantum simulation by enabling quantitative benchmarking of experimental snapshots from state-of-the-art quantum simulators.

cond-mat.str-el↗

arXiv2026

LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have been proposed, with learning-based approaches offering high accuracy and automation. However, they often require substantial ground truth, suffer from low training efficiency, lack support for basic trust properties, and provide limited explainability. Large Language Models (LLMs) offer a compelling alternative due to their strong zero-/few-shot reasoning abilities and broad knowledge. To this end, we propose LLM4Trust, the first benchmark framework that systematically explores the capabilities of LLMs for trust evaluation. We first construct diverse trust graphs to model five basic trust properties and design corresponding property understanding tasks. We then assess the ability of eight representative LLMs to understand these properties under nine prompt methods. Based on this exploration, we identify the most effective LLM-prompt combinations and apply them to five real-world datasets for validating LLMs' trust evaluation capability. During this process, we propose two strategies to extract key information from large-scale trust graphs, addressing the context window limitations of LLMs. Extensive experiments show that LLMs can effectively understand basic trust properties and have great potential for real-world trust evaluation, particularly under limited supervision. However, they remain vulnerable to attacks targeting trust graphs and demonstration examples used in few-shot prompting, and incur high inference costs. Accordingly, we propose a defense mechanism and batch inference to improve the robustness and efficiency of LLM-based trust evaluation. The source code of LLM4Trust is available at https://github.com/Jieerbobo/LLM4Trust

cs.LG↗

arXiv2026

Lee--Yang Zeros and Pseudocritical Drift in J-Q Néel Antiferromagnet--Valence-Bond Solid Transitions

Square-lattice J-Q models provide a sign-problem-free setting for probing the quantum phase transition between Néel antiferromagnet and columnar valence-bond solid. We analyze this transition through the scaling of Lee--Yang zeros, computed within stochastic series expansion quantum Monte Carlo by reweighting configurations sampled near criticality in the presence of complex source fields. Benchmark studies of the dimerized Heisenberg model and the checkerboard J-Q model validate the method, yielding stable $\mathrm{O}(3)$ critical scaling in the former and clear spacetime-volume scaling in the latter, as expected for a first-order transition. Applying the same analysis to the J-Q models, we find a pronounced and systematic drift of the leading-zero scaling with increasing system size, consistent with an extended pseudocritical regime. The Lee--Yang scaling implies an effective scaling dimension $Δ_ϕ$ of the $\mathrm{SO}(5)$ order-parameter field that decreases with size and is consistent with vanishing in the thermodynamic limit. Such behavior lies below the scalar unitarity bound of any unitary relativistic conformal field theory in $2{+}1$ dimensions and enforces inverse spacetime-volume scaling of the zeros, the hallmark of a first-order transition. These results support a weakly first-order interpretation of the Néel--VBS transition and establish the finite-size Lee--Yang zeros as a sensitive, symmetry-resolved diagnostic of pseudocriticality and transition order in the J-Q family.

cond-mat.str-el↗

arXiv2026

Criticality on Rényi Defects at (2+1)$D$ O(3) Quantum Critical Points

At a quantum critical point, the universal scaling behavior of Rényi entanglement entropy is controlled by the universality class of the codimension-two Rényi (or conical) defects in the infrared theory. In this work we perform a systematic study of critical correlations along Rényi defect lines in (2+1)$D$ quantum spin models realizing quantum phase transitions described by the O(3) Wilson-Fisher universality class, using large-scale quantum Monte Carlo simulations. We present numerical evidence that, for a fixed Rényi index $n$, there exist multiple Rényi defect universality classes, with distinct critical exponents for the O(3) order parameter on the defect. These universality classes are realized by choosing microscopically different entanglement cuts in lattice models, which we classify as ordinary, special and extraordinary according to their relation to surface criticality. For the extraordinary entanglement cut, we further find evidence for a phase transition on the defect as a function of the Rényi index. Our results highlight the key role of defect universality classes in determining the universal scaling of Rényi entropy, and provide a framework for understanding the previously observed dependence of Rényi entropy scaling on microscopic lattice details.

cond-mat.str-el↗

arXiv2026

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction, owing to their ability to learn expressive node representations that capture intricate trust relationships within a network. However, current GNN-based trust prediction models face several limitations: (i) Most of them fail to capture trust dynamicity, leading to questionable inferences. (ii) They rarely consider the heterogeneous nature of real-world networks, resulting in a loss of rich semantics. (iii) None of them support context-awareness, a basic property of trust, making prediction results coarse-grained. To this end, we propose CAT, the first Context-Aware GNN-based Trust prediction model that supports trust dynamicity and accurately represents real-world heterogeneity. CAT consists of a graph construction layer, an embedding layer, a heterogeneous attention layer, and a prediction layer. It handles dynamic graphs using continuous-time representations and captures temporal information through a time encoding function. To model graph heterogeneity and leverage semantic information, CAT employs a dual attention mechanism that identifies the importance of different node types and nodes within each type. For context-awareness, we introduce a new notion of meta-paths to extract contextual features. By constructing context embeddings and integrating a context-aware aggregator, CAT can predict both context-aware trust and overall trust. Extensive experiments on three real-world datasets demonstrate that CAT outperforms five groups of baselines in trust prediction, while exhibiting strong scalability to large-scale graphs and robustness against both trust-oriented and GNN-oriented attacks.

cs.LG↗

arXiv2026

A packet-level digital hardware twin for commissioning megahertz diagnostic edge AI and plasma control system integration in tokamaks

High-bandwidth plasma diagnostics increasingly provide inputs to machine learning and signal-processing algorithms intended for real-time tokamak control, but the complete path from diagnostic sampling to control-system handoff is difficult to commission because of their sampling rates. We develop a packet-level digital hardware twin for the megahertz diagnostic edge-AI architecture. The simulator represents a 64-channel, 1 MHz beam emission spectroscopy (BES) diagnostic embedded in a 96-channel dual-carrier acquisition system, two 48-channel streams with SPAD0 sample counters, 10 GbE Hardware UDP transport, packet loss and network jitter, FPGA parsing and dual-carrier alignment, causal preprocessing and edge inference, a compact Ethernet result packet, receiver-side shared state, and a 1 kHz PCS-like control cycle. Binary UDP payloads and PCAP files are generated rather than emulating transport only at the array level. With a baseline of 20 samples per packet, each carrier generates 50,000 packets s$^{-1}$ and 100 MB s$^{-1}$ of user payload. A 110 ms reference run produces 11,000 HUDP packets; an intentionally dropped 20-sample packet is detected by the sample-counter continuity logic and invalidates the two overlapping 128-sample inference windows without silent interpolation. For valid windows, the configured engineering latency model gives a median last-input-to-shared-memory latency of 91.6 us and a 99th percentile of 108.1 us. A separate operating-system loopback test sends binary FPGA-result datagrams through a UDP receiver into POSIX shared memory and preserves packet sequence and CRC for 20/20 packets. Interactive GUI interfaces expose timing, packetization, network faults, inference thresholds, and control-state inspection. The framework provides a reproducible environment for testing diagnostic-to-accelerator interfaces and fail-safe behavior for deployment on fusion devices.

physics.plasm-ph↗

arXiv2026

Thermal quasi-Devil's staircase in an anisotropic triangular-lattice Rydberg array

A Devil's staircase is a sequence of transitions between topological sectors as a control parameter is varied. Such staircases are predicted in frustrated magnets and lattice gauge theories, but observing them is hard: the winding number that labels each sector is topologically protected, so local quantum dynamics alone cannot move the system from one step to the next. Here we propose and analyze an experimentally feasible way to realize a thermal quasi-Devil's staircase in a finite Rydberg-atom array. We study the anisotropic triangular-lattice Ising antiferromagnet using a dimer mapping, a directed-string description, exact thermodynamics, and Monte Carlo simulations. In the defect-free string manifold, the commensurate--incommensurate onset follows from a simple energy--entropy balance, and the exact Ising critical relation reduces to the same condition when triangle-rule defects are rare. On a finite lattice, this onset appears as a quasi-Devil's staircase of winding-sector crossovers, which we resolve through winding distributions, structure factors, and directional correlations. The key point is that thermal fluctuations overcome the topological barriers that block sector changes under purely quantum dynamics, and the staircase exists precisely at the finite sizes and tunable effective temperatures of current Rydberg platforms. This makes the effect directly observable in existing experiments. Our comparison also shows that the finite-size steps are not phase transitions: in the thermodynamic limit only a single phase transition survives, and the system crosses over to a defect-dominated paramagnet.

cond-mat.str-el↗

arXiv2026

Collective Symmetry and Criticality from Single-Component Fluctuations

Symmetry plays a fundamental role in modern physics by guiding the classification of phases and the construction of effective theories of critical systems. Identifying collective symmetry remains challenging because tests based on preselected order parameters can overlook hidden components. A central goal is therefore to extract symmetry and critical information from a single accessible component. We pursue this goal using Lee--Yang zero ratios generated by a single ordering field. Unmeasured components shape the projected fluctuation distribution and hence the relative zero positions. Deep in a phase with spontaneous symmetry breaking, we show analytically that the zero ratios for an isotropic order parameter approach a parameter-free Bessel sequence fixed by the number of collective components. Near quantum criticality, fluctuations deform this distribution. The resulting ratio deviations and their finite-size coupling dependence carry additional critical information. We demonstrate these results with large-scale quantum Monte Carlo simulations of many-body systems with conventional and enlarged continuous symmetries. This approach provides quantitative access to collective symmetry and criticality without measuring every competing order.

cond-mat.stat-mech↗

arXiv2026

Quantum Monte Carlo in the Age of Many-Body Quantum Information

Quantum Monte Carlo (QMC) methods are among the central numerical tools for studying strongly correlated quantum many-body systems, particularly in higher dimensions. As quantum information has introduced new information-theoretic perspectives and diagnostics into many-body physics, QMC methods have accordingly been extended beyond the measurement of conventional linear observables. This review summarizes recent progress in adapting QMC to many-body quantum-information, focusing on qubit or spin-$1/2$ systems as a concrete setting while keeping the discussion broadly applicable to qudit and bosonic systems. We present a unified perspective on the extraction of nonlinear diagnostics, including entanglement entropies and entanglement spectra, Rényi negativities for mixed-state entanglement, stabilizer entropies for quantum magic, and decoherence-driven phenomena such as the interplay between imaginary-time evolution and decoherence and strong-to-weak spontaneous symmetry breaking.

quant-ph↗

arXiv2026

Resilient Concurrent Causal Discovery for Topological Event Sequences

Causal discovery on topological event sequences is crucial for ensuring the reliability of networks. However, existing methods struggle to capture the complex causal relationships arising from concurrent events and lack robustness to incomplete event sequences. To address these issues, we propose a resilient concurrent causal discovery method, termed RCCD, enabling robust learning of causal graphs from topological event sequences. Specifically, we first introduce an influence-aware hyperedge causal attention mechanism, which incorporates event duration into the embedding representation, aggregates concurrent event features via hyperedge causal convolution, and injects network prior knowledge to capture the complex many-to-one causal interactions. Furthermore, we design a masked-based alternating causal optimization framework, which forces the model to recover masked event types based on context through self-supervised mask reconstruction, thereby enhancing the resilience of the predictor to missing data. To validate the effectiveness of our method, we conduct extensive experiments on both simulated and real-world telecommunication network datasets. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in both accuracy and robustness, making it more suitable for real-world telecommunication network environments.

cs.LG↗

arXiv2026

Scaling of the disorder operator at (3+1)D O(3) quantum criticality

The disorder operator, as an easily measured nonlocal observable, displays great potential in detecting intrinsic information of field theories. It has been systematically studied in one- and two-dimensional (1D and 2D) quantum systems, while the knowledge of 3D is still limited. The disorder operator associated with U(1) global symmetry exhibits rich geometric dependence on the shape of the spatial region at a quantum critical point, meanwhile, (3+1)D is the upper critical dimension for O(N) criticality, both of which pose a challenge for exploring the disorder operator in high dimensions. In this Letter, we investigate the scaling behaviors of disorder operators in (3+1)D O(3) models through large-scale quantum Monte Carlo simulation combined with theoretical analysis. Although the upper critical dimension introduces logarithmic corrections in correlations, we analytically prove and numerically demonstrate that the corrections do not modify the universal trihedral-corner contribution of the disorder operator. The universal contributions, such as the current central charge, have been revealed in our calculation, which establishes a concrete link between lattice simulations and continuum field theory. This work opens promising directions for the experimental and numerical exploration of universal properties at quantum critical points in (3+1)D models.

cond-mat.str-el↗

arXiv2026

Kosterlitz--Thouless Criticality in a Dipole-Conserving XY Model

In this Letter, we study finite-temperature phase transitions in a two-dimensional classical statistical model called ``dipole-conserving XY model''. Unlike the conventional XY model, the original phase field $θ$ has no quasi-long-range order, conventional phase vortices have finite self-energy, and the standard helicity modulus vanishes identically. Analytic vortex energetics and Gaussian continuum theory show that Kosterlitz--Thouless (KT) criticality is instead controlled by phase-gradient (PG) vortices defined in the two compact dipole fields $χ_α=a\partial_αθ$, whose self-energies grow logarithmically with system size. We determine the phase diagram using parallel-tempering Metropolis Monte Carlo simulations and three diagnostics tailored to dipole conservation. KT finite-size scaling of dipole-field correlation-ratio crossings locates the critical temperatures; generalized helicity moduli defined through quadratic phase twists measure the stiffness of individual dipole channels; and PG-vortex densities obtained from plaquette winding numbers identify the proliferating vortex species. At isotropic couplings, the two PG-vortex species unbind simultaneously at a single KT transition. Spatial anisotropy separates their unbinding temperatures, yielding two KT transitions and an intermediate phase with quasi-long-range order in only one dipole channel. The transitions merge again when the mixed-derivative channel is removed. Our results establish the finite-temperature phase structure of the dipole-conserving XY model and identify thermal PG-vortex unbinding as a novel route to KT criticality in systems with higher-moment conservation.

cond-mat.quant-gas↗

arXiv2026

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-level differential privacy, a common approach is to inject noise into all elements of the graph's adjacency matrix, thereby obfuscating the existence of any single edge. However, stronger privacy requires more noise, and excessive noise reduces utility, making the privacy-utility balance a major barrier to practical privacy-preserving graph learning. To address this issue, we propose EdgeRefine, a local differential privacy framework that improves this trade-off through adaptive edge refinement. EdgeRefine first estimates edge-existence probabilities using Jaccard similarity and ranks edges for noisy edge removal. To ensure the sparsity and reliability of the final graph, it uses the privacy budget $ε$ to determine the ratio of true to false edges, samples them separately based on this probability ranking, and controls the total number of edges with a separate sampling rate $k$. Extensive experiments show that EdgeRefine achieves accuracy comparable to the noise-free baseline and substantially outperforms other privacy-preserving methods across datasets and GNN architectures. Under privacy budget $ε= 2.5$, EdgeRefine improves node classification accuracy over state-of-the-art baselines by 17.8\% on ACM under GAT and 19.7\% on Cora under GCN. In graph classification, it achieves an average accuracy degradation of around 5\% compared to the noise-free baseline. Under graph reconstruction attacks, EdgeRefine maintains relative absolute error levels above 1 across all privacy budgets, averaging 1.962 on Cora and 1.472 on AMAP, indicating strong resilience against privacy leakage.

cs.LG↗

arXiv2026

Finite-time Scaling of the surface special transition in a 3D classical Heisenberg model

We investigate nonequilibrium driven dynamics across the special surface phase transition in the three-dimensional classical Heisenberg model with open boundaries, where tuning the surface coupling gives access to an extraordinary-log boundary critical state characterized by logarithmic, rather than power-law, decay of correlations. Using Monte Carlo simulations, we realize four driving protocols: temperature heating and cooling across the special transition, and surface-coupling ramps from the ordinary and extraordinary-log critical states into the special point. For temperature-driven protocols, the surface order parameter obeys a generalization of the finite-time scaling (FTS) and the Kibble-Zurek mechanism. The central finding emerges when the system is driven from the extraordinary-log critical state: the large-rate scaling relation acquires a logarithmic correction and takes the novel form $M^{2}_{s}\propto R^{(1+η_{s})/r_{s}}[\log(LR^{1/η_{s}})]^{-q}$ , where $R$ is the driving rate, $L$ the system size, $η_{s}$ the surface anomalous dimension, $r_{s}$ the scaling dimension of $R$, and $q$ the exponent governing the logarithmic boundary criticality. We demonstrate that this form follows from the general FTS framework by incorporating the logarithmic initial-state memory, and we achieve excellent data collapse over a wide range of system sizes and driving rates. Our results establish that extraordinary-log initial states alter nonequilibrium critical scaling, extending boundary FTS beyond conventional power-law initial conditions.

cond-mat.stat-mech↗