arXiv subjects

Kan He

Publications and source records attributed to Kan He.

At least 19 recordsLinked to original sources

arXiv2026

Verifying full quantum network nonlocality in arbitrary configurations by nonlinear Bell-like inequalities

Full quantum network nonlocality (FQNN) describes a scenario where all sources in a network are nonlocal. Existing criteria of FQNN can only be verified in star networks by violating a single Bell-like inequality. Here we propose a method that certifies FQNN in general quantum networks using only a single Bell-like inequality. We show that the topological obstacle to one-shot detection can be overcome by expanding the original network with a carefully chosen number of auxiliary local sources and parties. The correlations of the enlarged network are then tested with a single inequality; a violation implies that all original sources must be nonlocal. Our approach provides an efficient, experimentally friendly way to verify FQNN in any network topology.

quant-ph↗

arXiv2026

SCARA: A Semantics-Constrained Autonomous Remediation Agent for Opaque Industrial Software Vulnerabilities

Critical-infrastructure operators are increasingly expected to assess and remediate vulnerabilities in deployed industrial software. However, much of this software exists as opaque industrial software (OIS), including stripped firmware, proprietary protocol handlers, and compiled control logic without source code, symbols, build environments, or hardware interfaces. While binary analysis can identify vulnerability candidates, existing automated repair systems largely rely on source code, compilable artifacts, sanitizer feedback, or instrumentable builds, leaving a gap between binary-level discovery and validated remediation. This paper presents SCARA, a Semantics-Constrained Autonomous Remediation Agent for OIS. SCARA operates under a source-unavailable defender model and connects upstream binary vulnerability candidates to conditionally validated remedies through a four-stage pipeline. Operational-state-aware verification (OSVA) filters infeasible candidates using a nine-component industrial state model; remediation synthesis (RSA) selects the strongest available remedy across protocol mitigation, binary hardening, and SSCKG-constrained source patches; and correctness validation (CVA) provides conditional correctness evidence via behavioral-coverage preservation, independent replay, and typed rejection feedback. On OIS-RemedBench, a 15-case benchmark spanning firmware, protocol handlers, and ICS/PLC artifacts, SCARA achieves observed 100% precision with no false positives, refutes 20.0% of cases as operationally infeasible, and reaches 88.9% remediation success after targeted reruns. To our knowledge, SCARA is the first end-to-end framework that connects binary vulnerability candidates to conditionally validated remediation for opaque industrial software.

cs.CR↗

arXiv2026

Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software

Automated vulnerability detection in critical-infrastructure software confronts a fundamental barrier: industrial software is routinely deployed as stripped, symbol-free binaries that deprive conventional Software Composition Analysis of the source-level transparency it requires. Existing binary analysis techniques close this Semantic Gap only partially -- graph-based detectors preserve structural syntax but discard behavioral semantics, while large language models supply rich semantic cues at the cost of unstable, hallucination-prone inference. To address this gap, we present a semantic-enhanced neuro-symbolic framework that reconstructs behavioral semantics directly from opaque binaries and performs tractable global risk reasoning. Three tightly coupled mechanisms drive this capability: (1) abstract interpretation combined with a reflexive prompting pipeline that structurally constrains a local LLM agent, effectively suppressing hallucinations; (2) a surjective transformation that compresses raw Code Property Graphs into typed Software Supply Chain Knowledge Graphs amenable to scalable reasoning; and (3) a domain-adapted Graphormer that captures long-range vulnerability propagation, augmented by embedding-space subgraph matching to uncover zero-day and APT-style attack patterns. Evaluated across three benchmarks of increasing domain specificity, the framework consistently outperforms all baselines on detection accuracy, semantic lifting fidelity, and APT fingerprint matching. Deployment on a hybrid virtual-physical testbed incorporating production-grade hardware from five ICS vendors further confirms strong detection coverage of high-impact CVEs while substantially reducing false-positive rates relative to leading commercial tools.

cs.SE↗

arXiv2026

Mutually-commuting von Neumann algebra models of quantum networks and violation of Bell-type inequalities

Employing mutually-commuting von Neumann algebras to represent the algebra of observables on quantum systems provides a framework for studying quantum information theory in systems with infinite degrees of freedom and quantum field theory, yielding many profound results that differ from non-relativistic quantum systems. In this paper, we establish a mutually-commuting von Neumann algebra model of quantum networks with arbitrary structures. We derive Bell-type inequalities on this model, and determine various bounds for Bell-type inequalities based on the structure of underline von Neumann algebras, and identify the algebraic structural conditions required for their violation. The conditions on the algebraic structure of observables for maximal violation of Bell-type inequalities, which we discovered in the context of von Neumann algebra models, can in turn guide the search for measurements in the non-relativistic setting.

quant-ph↗

arXiv2026

Violation of Bell-type Inequalities on Mutually-commuting von Neumann Algebra Models of Entanglement Swapping Networks

Violation of Bell inequalities in bipartite systems represented by mutually-commuting von Neumann algebras has pioneered the study of vacuum entanglement in algebraic quantum field theory. It is unexpected that the maximal violation of Bell inequality can discover algebraic structures. In the paper, we establish the mutually-commuting von Neumann algebra model for entanglement swapping networks and Bell-type inequalities on this model. It generalizes the bipartite case to the ternary case. These algebras are all general von Neumann algebras, which provide a natural perspective to investigate Bell nonlocality in quantum networks in the infinitely-many-degree-of-freedom setting. We determine various bounds for Bell-type inequalities based on the structure of von Neumann algebras, and identify the algebraic structural conditions required for their violation. Finally, we show that the maximal violation of Bell-type inequalities in entanglement swapping networks can be used to determine partially the type classification of the underlying von Neumann algebras.

math.FA↗

arXiv2025

$k$-Entanglement Measure for Multipartite Systems without Convex-Roof Extensions and its Evaluation

Multipartite entanglement underpins quantum technologies but its study is limited by the lack of universal measures, unified frameworks, and the intractability of convex-roof extensions. We establish an axiomatic framework and introduce the first \emph{true} $k$-entanglement measure, $E_w^{(k,n)}$, which satisfies all axioms, establishes $k$-entanglement as a multipartite quantum resource, avoids convex-roof constructions, and is efficiently computable. A universal algorithm evaluates arbitrary finite-dimensional states, with open-source software covering all partitions of four-qubit systems. Numerical tests certify $k$-entanglement within 200 seconds, consistent with necessary-and-sufficient criteria, tightening bounds and revealing new thresholds. This framework offers a scalable, practical tool for rigorous multipartite entanglement quantification.

quant-ph↗

arXiv2025

TRAM: A Transverse Relaxation Time-Aware Qubit Mapping Algorithm for NISQ Devices

Noisy intermediate-scale quantum (NISQ) devices impose dual challenges on quantum circuit execution: limited qubit connectivity requires extensive SWAP-gate routing, while time-dependent decoherence progressively degrades quantum information. Existing qubit mapping algorithms optimize for hardware topology and static calibration metrics but systematically neglect transverse relaxation dynamics (T2), creating a fundamental gap between compiler decisions and evolving noise characteristics. We present TRAM (Transverse Relaxation Time-Aware Qubit Mapping), a coherence-guided compilation framework that elevates decoherence mitigation to a primary optimization objective. TRAM integrates calibration-informed community detection to construct noise-resilient qubit partitions, generates time-weighted initial mappings that anticipate coherence decay, and dynamically schedules SWAP operations to minimize cumulative error accumulation. Evaluated on Qiskit-based simulators with realistic noise models, TRAM outperforms SABRE by 3.59% in fidelity, reduces gate count by 11.49%, and shortens circuit depth by 12.28%, establishing coherence-aware optimization as essential for practical quantum compilation in the NISQ era.

quant-ph↗

arXiv2025

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols

Industrial control systems (ICS) are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through weaknesses in their communication protocols. This paper presents MALF (Multi-Agent LLM Fuzzing Framework), an advanced fuzzing solution that integrates large language models (LLMs) with multi-agent coordination to identify vulnerabilities in industrial control protocols (ICPs). By leveraging Retrieval-Augmented Generation (RAG) for domain-specific knowledge and QLoRA fine-tuning for protocol-aware input generation, MALF enhances fuzz testing precision and adaptability. The multi-agent framework optimizes seed generation, mutation strategies, and feedback-driven refinement, leading to improved vulnerability discovery. Experiments on protocols like Modbus/TCP, S7Comm, and Ethernet/IP demonstrate that MALF surpasses traditional methods, achieving a test case pass rate (TCPR) of 88-92% and generating more exception triggers (ETN). MALF also maintains over 90% seed coverage and Shannon entropy values between 4.2 and 4.6 bits, ensuring diverse, protocol-compliant mutations. Deployed in a real-world Industrial Attack-Defense Range for power plants, MALF identified critical vulnerabilities, including three zero-day flaws, one confirmed and registered by CNVD. These results validate MALF's effectiveness in real-world fuzzing applications. This research highlights the transformative potential of multi-agent LLMs in ICS cybersecurity, offering a scalable, automated framework that sets a new standard for vulnerability discovery and strengthens critical infrastructure security against emerging threats.

cs.CR↗

arXiv2025

Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model

Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical solvers are accurate, they are often computationally expensive and lack flexibility across varying initial conditions and geometries. Neural operators provide a data-driven alternative by learning solution operators between function spaces, but current architectures often fail to capture multiscale behavior and neglect underlying physical symmetries. Here we show that an equivariant U-shaped neural operator (E-UNO) can learn the evolution of the phase-field variable from short histories of past dynamics, achieving accurate predictions across space and time. The model combines global spectral convolution with a multi-resolution U-shaped architecture and regulates translation equivariance to align with the underlying physics. E-UNO outperforms standard Fourier neural operator and U-shaped neural operator baselines, particularly on fine-scale and high-frequency structures. By encoding symmetry and scale hierarchy, the model generalizes better, requires less training data, and yields physically consistent dynamics. This establishes E-UNO as an efficient surrogate for complex phase-field systems.

cs.LG↗

arXiv2025

Multipartite correlation measures and framework for multipartite quantum resources theory

In recent years, it has been recognized that properties of multipartite physical systems, such as genuine multipartite entanglement, can be considered as important resources for quantum information and other areas of physics. However, the current framework of multipartite quantum resource theory is flawed. In this paper, we propose a more reasonable framework for multipartite quantum resource theory with a particular focus on axiomatic definition for true measures of multipartite quantum correlations (MQC) that regulates how to measure the correlation in part systems (the unification condition) and how to describe the requirement from many-body resource theory that the correlation hold by part system does not exceed that of the entire system (the hierarchy condition). We find that, due to the inherent characteristics of MQCs, the true measures of different MQCs exhibit distinct hierarchy conditions. Based on this framework, we verify that multipartite entanglement, $k$-entanglement, $k$-partite entanglement, multipartite non-PPT, multipartite coherence, multipartite imaginarity, multipartite multi-mode Gaussian non-product correlation, multipartite multi-mode Gaussian imaginarity, and multipartite single-mode Gaussian coherence are all symmetric multipartite quantum resources. We also show that, multipartite steering is an asymmetric multipartite quantum resource. Finally, the monogamy relations for true measures of symmetric MQCs are discussed.

quant-ph↗

arXiv2025

Negativity Percolation in Continuous-Variable Quantum Networks

Quantum networks (QNs) have been predominantly driven by discrete-variable (DV) architectures. Yet, optical platforms naturally generate Gaussian states--the common states of continuous-variable (CV) systems, making CV-based QNs an attractive route toward scalable, chip-integrated quantum computation and communication. To bridge the gap between well-studied DV entanglement percolation theories and their CV counterpart, we introduce a Gaussian-to-Gaussian entanglement distribution scheme that deterministically transports two-mode squeezed vacuum states across large CV networks. Analysis of the scheme's collective behavior using statistical-physics methods reveals a new form of entanglement percolation--negativity percolation theory (NegPT)--characterized by a bounded entanglement measure called the ratio negativity. We discover that NegPT exhibits a mixed-order phase transition, marked simultaneously by both an abrupt change in global entanglement and a long-range correlation between nodes. This distinctive behavior places CV-based QNs in a new universality class, fundamentally distinct from DV systems. Additionally, the abruptness of this transition introduces a critical vulnerability of CV-based QNs: conventional feedback mechanism becomes inherently unstable near the threshold, highlighting practical implications for stabilizing large-scale CV-based QNs. Our results unify statistical models for CV-based entanglement distribution and uncover previously unexplored critical phenomena unique to CV systems, providing valuable insights and guidelines essential for developing robust, feedback-stabilized QNs.

quant-ph↗

arXiv2025

Witnessing nonlocality in quantum network of continuous-variable systems by generalized quasiprobability functions

Gaussian measurements can not be used to witness nonlocality in Gaussian states as well as the network nonlocality in networks of continuous-variable (CV) systems. Thus special non-Gaussian measurements have to be utilized. In the present paper, we first propose a kind of nonlinear Bell-type inequality that is applicable to quantum networks of both finite or infinite dimensional systems. Violation of the inequality will witness the network nonlocality. This inequality allows us to propose a method of the supremum strategy for detecting network nonlocality in CV systems with source states being any multipartite multi-mode Gaussian states according to the configurations of the networks by utilizing non-Gaussian measurements based on generalized quasiprobability functions. The nonlinear Bell-type inequalities for CV networks, which depend solely on the generalized quasiprobability functions of Gaussian states, are straightforward to construct and implement. As illustrations, we propose the corresponding nonlinear Bell-type inequalities for any chain, star, tree-shaped and cyclic networks in CV systems with source states being $(1+1)$-mode Gaussian states. The examples show that this approach works well for witnessing the nonlocality in networks of CV systems. Particularly, a thorough discussion is given for the entanglement swapping network. Our study provide a strong signature for the network nonlocality nature of CV systems and lead to precise recipes for its experimental verification.

quant-ph↗

arXiv2025

Reducing Complexity of Shadow Process Tomography with Generalized Measurements

Quantum process tomography (QPT) is crucial for advancing quantum technologies, including quantum computers, quantum networks and quantum sensors. Shadow process tomography (SPT) utilizes the Choi isomorphism to map QPT to shadow state tomography (SST), significantly reducing the sample complexity for extracting information from quantum processes. However, SPT relies on random unitary operators and complicates the determination of the optimal unitary operator that minimizes the shadow norm, which is the key factor influencing the sample complexity. In this work, we propose a generalized SPT framework that minimizes the shadow norm by replacing unitary operators with generalized measurements (POVMs). This approach, termed shadow process tomography with POVMs (POVM-SPT), uses convex optimization to identify the optimal POVM for minimizing the shadow norm, thereby further reducing sample complexity. We demonstrate the identification of the optimal POVM through numerical simulations and provide the corresponding optimization algorithms. Our numerical experiments demonstrate that POVM-SPT achieves a substantial reduction in shadow norm compared to conventional SPT, with an approximate 7-fold improvement for single-qubit input states and a remarkable $2^{180}$-fold enhancement for 64-qubit input states. These results reveal that POVM-SPT offers significant advantages in simplifying SPT tasks, particularly for large-scale quantum systems.

quant-ph↗

arXiv2024

A nonconvex entanglement monotone determining the characteristic length of entanglement distribution in continuous-variable quantum networks

Quantum networks (QNs) promise to enhance the performance of various quantum technologies in the near future by distributing entangled states over long distances. The first step towards this is to develop novel entanglement measures that are both informative and computationally tractable at large scales. While numerous such entanglement measures exist for discrete-variable (DV) systems, a comprehensive exploration for experimentally preferred continuous-variable (CV) systems is lacking. Here, we introduce a class of CV entanglement measures, among which we identify a nonconvex entanglement monotone -- the ratio negativity, which possesses a simple, scalable form that determines the exponential decay of optimal entanglement swapping on a chain of pure Gaussian states. This characterization opens avenues for leveraging statistical physics tools to analyze swapping-protocol-based CV QNs.

quant-ph↗

arXiv2024

Gaussian unsteerable channels and computable quantifications of Gaussian steering

The current quantum resource theory for Gaussian steering for continuous-variable systems is flawed and incomplete. Its primary shortcoming stems from an inadequate comprehension of the architecture of Gaussian channels transforming Gaussian unsteerable states into Gaussian unsteerable states, resulting in a restricted selection of free operations. In the present paper, we explore in depth the structure of such $(m+n)$-mode Gaussian channels, and introduce the class of the Gaussian unsteerable channels and the class of maximal Gaussian unsteerable channels, both of them may be chosen as the free operations, which completes the resource theory for Gaussian steering from $A$ to $B$ by Alice's Gaussian measurements. We also propose two quantifications $\mathcal{J}_{j}$ $(j=1,2)$ of $(m+n)$-mode Gaussian steering from $A$ to $B$. The computation of the value of $\mathcal{J}_{j}$ is straightforward and efficient, as it solely relies on the covariance matrices of Gaussian states, eliminating the need for any optimization procedures. Though $\mathcal{J}_{j}$s are not genuine Gaussian steering measures, they have some nice properties such as non-increasing under certain Gaussian unsteerable channels. Additionally, we compare ${\mathcal J}_2$ with the Gaussian steering measure $\mathcal N_3$, which is based on the Uhlmann fidelity, revealing that ${\mathcal J}_2$ is an upper bound of $\mathcal N_3$ at certain class of $(1+1)$-mode Gaussian pure states. As an illustration, we apply $\mathcal J_2$ to discuss the behaviour of Gaussian steering for a special class of $(1+1)$-mode Gaussian states in Markovian environments, which uncovers the intriguing phenomenon of rapid decay in quantum steering.

quant-ph↗

arXiv2024

A norm inequality on noncommutative symmetric spaces related to a question of Bourin

In this note, we study a question introduced by Bourin \cite{2009Matrix} and partially solve the question of Bourin. In fact, for t\in[0,\frac{1}{4}]\cup[\frac{3}{4},1], we show that |||x^{t}y^{1-t}+y^{t}x^{1-t}|||\leq|||x+y|||, where x,y\in\mathbb{M}_{n}(\mathbb{C})^+ and \||\cdot\|| is the unitarily invariant norm. Moreover, we prove that the above inequality holds on noncommutative fully symmetric spaces.

math.FA↗

arXiv2023

Star network non-n-local correlations can resist consistency noises better

Imperfections from devices can result in the decay or even vanish of non-n-local correlations as the number of parties n increases in the polygon and linear quantum networks ([Phys. Rev. A 106, 042206 (2022)] and [Phys. Rev. A 107, 032404 (2023)]). Even so this phenomenon is also for the special kind of noises, including consistency noises of a sequence of devices, which means the sequence of devices have the same probability fails to detect. However, in the paper, we discover that star network quantum non-n-local correlations can resist better consistency noises than these in polygon and linear networks. We first calculate the noisy expected value o f star network non-n-locality and analyze the persistency conditions theoretically. When assume that congener devices have the consistency noise, the persistency number of sources n has been rid of such noises, and approximates to the infinity. Polygon and linear network non-n-local correlations can not meet the requirements. Furthermore, we explore the change pattern of the maximal number of sources nmax such that non-nmax-local correlation can be demonstrated in the star network under the influence of partially consistent noises, which is more general than consistent ones.

quant-ph↗

arXiv2022

Violating the bilocal inequality with separable mixed states in the entanglement-swapping network

It has been showed that two entangle pure states can violate the bilocal inequality in the entanglement-swapping network, vice versa. What happens for mixed states? Whether or not are there separable mixed states violating the bilocal inequality? In the work, we devote to finding the mixed Werner states which violate the bilocal inequality by Particle Swarm Optimization (PSO) algorithms. Finally, we shows that there are pairs of states, where one is separable and the other is entangled, can violate the bilocal inequality.

quant-ph↗