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Boyang Chen

Publications and source records attributed to Boyang Chen.

At least 19 recordsLinked to original sources
arXiv2026

Gate-Efficient Implementation of the Query-Optimal Time-Dependent Hamiltonian Simulation

The query-optimal algorithm of [CGWZ26] for general time-dependent Hamiltonian simulation uses $$ q = O\left( \alpha T + \frac{\log(1/\varepsilon)}{\log\left(e + \log(1/\varepsilon)/(\alpha T) \right)} \right) $$ queries to $\mathrm{HAM\mbox{-}T}$ within $\varepsilon$ error for a Lipschitz-continuous time-dependent Hamiltonian $H(t)$ on $[0,T]$ satisfying $\left\lVert H(t)\right\rVert\leq\alpha$. However, its direct circuit implementation incurs a substantially larger gate overhead. In this note, we give an implementation of the same algorithm that retains its optimal query complexity and uses $$ O\left[ q \left( a + \log\left(1 + \frac{T(\alpha + \beta T)}{\varepsilon} \right) \right) \right] $$ one- and two-qubit gates, where $a$ is the number of block-encoding ancilla qubits and $\beta$ is the Lipschitz constant of $H$. The main ingredient is an exact dyadic factorization of the ordered update product in the underlying one-query transducer.

quant-ph↗
arXiv2026

Quantum Pessiland

Pessiland is a world where NP is hard on average but one-way functions (OWFs) do not exist [Impagliazzo 1995]. Because almost all classical cryptographic primitives imply OWFs [Impagliazzo and Luby 1989], there is almost no classical cryptography in Pessiland. On the other hand, quantum cryptography can exist even when OWFs do not [Kretschmer 2021; Morimae and Yamakawa 2022; Ananth, Qian and Yuen 2022]. Is there a quantum analogue of Pessiland where NP is hard on average but even quantum cryptography does not exist? In this paper, we show that such a miserable world, Quantum Pessiland, exists: there is a quantum oracle relative to which $UP\cap coUP$ is hard on average against quantum polynomial-time algorithms with quantum advice, yet auxiliary-input EFI pairs do not exist. We also show that there is a classical oracle relative to which $UP\cap coUP$ is hard on average against quantum polynomial-time algorithms with quantum advice, yet classically-secure auxiliary-input one-way puzzles (OWPuzzs) do not exist. Almost all quantum cryptographic primitives imply EFI pairs or OWPuzzs, and therefore these results mean that there is almost no quantum cryptography relative to these oracles. We further show that relative to the classical oracle, SampBQP = SampBPP, and therefore there is no sampling-based quantum advantage in Quantum Pessiland. Finally, because our average-case hardness of $UP\cap coUP$ implies $P^{\#P}\not\subseteq i.o.BQP/qpoly$, our result also implies that a non-relativizing proof technique is necessary to construct OWPuzzs solely from $P^{\#P}\not\subseteq i.o.BQP/qpoly$, which gives a partial negative answer to the open problem of [Khurana and Tomer 2025].

quant-ph↗
arXiv2026

SynChain: Inducing Computer-Use Agent Systems to Construct Their Own Attack Chains

Computer-use agents~(CUAs) have transformed large language models into persistent execution systems capable of generating, storing, and reusing artifacts like skills and memory entries. However, existing security defenses largely treat attacks as externally triggered or temporally bounded, leaving a critical gap in addressing how compromise can propagate internally through an agent's own persistent state. We reveal that malicious influence can be covertly embedded into the structural redundancies of autonomously synthesized artifacts, allowing it to survive internal state updates and bypass standard vetting mechanisms. To formalize this threat, we introduce SynChain, a self-synthesized attack paradigm utilizing persistence-aware directed supervised fine-tuning to induce agents to create poisoned yet benign-looking artifacts. To systematically evaluate this propagation, we construct CUAChain, a dataset comprising 30 benign task chains and three attack objectives. SynChain enables dormant payloads to seamlessly reactivate in future workflows as trusted context, operating entirely without new malicious exogenous inputs. Extensive experiments on OpenClaw, Codex, and Claude Code under four defense settings demonstrate that SynChain achieves high attack success and outperforms adapted baselines, proving that securing CUAs requires provenance-aware reasoning over cross-task execution trajectories.

cs.CR↗
arXiv2026

Accurate simulation of delamination with a resin-rich layer-dependent penalty stiffness based on structural cohesive elements

Shell-based cohesive elements tend to overestimate the compression ahead of the crack tip because of approximations in the penalty stiffness. In this study, the higher-order structural cohesive element previously developed by the authors is enhanced with a resin-rich layer-dependent penalty stiffness to improve both computational efficiency and predictive accuracy. The proposed formulation distinguishes between the normal and shear penalty stiffnesses. It extends the resin-rich layer-based penalty stiffness from the layer-wise to the equivalent single-layer framework. This extension is achieved using the through-thickness distributions of the out-of-plane normal and transverse shear stresses derived from beam theory. The proposed method is verified and validated against benchmark problems for Mode I, Mode II, mixed-mode, and reinforced DCB configurations. Compared with the conventional formulation, it exhibits significantly improved mesh convergence and provides more accurate predictions of the compression distribution ahead of the crack tip and the delamination propagation.

cs.CE↗
arXiv2026

Time-Dependent Hamiltonian Simulation with Optimal Query Complexity

We give a query-optimal algorithm for simulating a general $n$-qubit time-dependent Hamiltonian $H(t)$ on $[0,T]$, assuming that $H$ is Lipschitz continuous and $\|H(t)\|\leq\alpha$. In the standard $\mathrm{HAM\mbox{-}T}$ access model, the algorithm approximates the time-ordered propagator $U_H(T)$ to error $\varepsilon$ using $$ O\left( \alpha T+\frac{\log(1/\varepsilon)} {\log(e+\log(1/\varepsilon)/(\alpha T))} \right) $$ $\mathrm{HAM\mbox{-}T}$ queries. This matches the known query lower bound for time-independent Hamiltonians, showing that time dependence incurs no asymptotic query overhead. Our method first constructs a one-query transducer that, given an auxiliary state, implements an approximation to $U_H(T)$ and returns the state unchanged. A weighted combination of circuits that apply the transducer different numbers of times makes the error caused by omitting this state decay factorially, yielding the stated optimal precision dependence. For time-independent Hamiltonians, the same method also gives a query-optimal alternative to qubitization.

quant-ph↗
arXiv2026

Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle

Once visual content enters an AI pipeline, its owner often retains little technical control over how it is used. Legal and regulatory remedies can address misuse, but many technical interventions must be applied earlier, when content is released or accessed. This survey examines the protective paradigm that has grown around this intervention point, which we call \emph{adversarial attacks for good}. Perturbations and structured signals long studied as attacks on learned models are instead applied by data owners, creators, platforms, or auditors to disrupt unauthorized automation or support later accountability. Five research communities have arrived at this inversion largely independently, each addressing a different stage of a visual asset's lifecycle: privacy filters against unwanted recognition at sharing time, unlearnable examples against unauthorized training, generative safeguards against malicious editing or imitation, adversarial CAPTCHAs for access control against automated agents, and provenance mechanisms for post-circulation attribution. Although developed in separate venues with incompatible success criteria, many of these methods exploit persistent gaps between human perception, semantic interpretation, and machine inference, suggesting that the paradigm remains relevant as visual pipelines evolve toward multimodal models and autonomous agents. To make their claims comparable, we evaluate all five families along shared axes of transferability, adaptability, and deployment readiness. Across the lifecycle, we find that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce. We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.

cs.CR↗
arXiv2026

A new approach for the determination of through-thickness and free-edge stresses in composite laminates based on structural elements

Thin shell elements based on the Kirchhoff-Love hypothesis account for only three stress components: the in-plane normal stresses and the in-plane shear stress. The out-of-plane stress components required by conventional three-dimensional damage criteria are unavailable. As a result, damage initiation in composite laminates cannot be accurately predicted. This paper presents a method for capturing free edge effects in multilayer structural elements with arbitrary composite layups based on the Kirchhoff-Love hypothesis.The proposed formulation employs structural cohesive elements to model composite laminates and uses an accurate penalty stiffness derived from the laminate's resin-rich layers. By accurately characterizing the interfacial mechanical response, out-of-plane stress components can be recovered.

cs.CE↗
arXiv2026

MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting

Current quantum computers do not yet have the required qubit resources to meet the demands of most practical quantum algorithms. To circumvent this constraint, the practice of dividing these algorithms into parts through quantum circuit cutting has been explored. Many of these works either show exponential scaling or are far from optimal solutions. In this paper, MosaiQC is presented as a novel framework to improve upon existing circuit cutting frameworks. A hybrid warmstart with refinement optimization is used to find cutting solutions, allowing the combination of both wire and gate cuts. Additionally, MosaiQC enables hardware partitions of mixed sizes. Furthermore, the refinement stage incorporates a fast approximate quadratic assignment solver to better place hardware partitions, demonstrating a mean local fidelity improvement of $19.56 \% \pm 6.17\%$ over the baseline algorithm. In runtime and sampling overhead costs, improvements of $2.88 \times$ and an average of $16.84\%$ cut reduction (resulting in an average $5.83 \cdot 10^{11} \times$ overhead reduction) are observed. MosaiQC demonstrates a superior trade-off for run speed and solution quality, while adding fundamental features excluded by most competitors. With this, MosaiQC demonstrates that scalable heuristic optimization can substantially reduce the computational overhead of circuit-cut placement for increasingly large quantum circuits.

quant-ph↗
arXiv2026

On the Limits of Stretching Quantum Pseudorandomness

Pseudorandom states, introduced by Ji, Liu, and Song (CRYPTO '18), are quantum analogues of classical pseudorandom generators. A fundamental property of classical pseudorandom generators is that their output can be stretched to arbitrary polynomial length. Whether an analogous stretching property holds for quantum pseudorandom states remains unclear. In this work, we prove the first black-box separation between single-copy secure pseudorandom states ($\mathsf{1PRS}$) with different output lengths. Specifically, we construct a quantum oracle relative to which $\mathsf{1PRS}$ with output length $m(n)=1.1n$ exist, but $\mathsf{1PRS}$ with output length $m(n)=\Omega(n^{2+\epsilon})$ do not, for any $\epsilon>0$. Our proof leverages the Common Haar Random State (CHRS) model introduced by Chen, Coladangelo, and Sattath (EUROCRYPT '25), and introduces a technique to bound the effective number of resource CHRS states utilized by any $\mathsf{1PRS}$ generator in this model.

quant-ph↗
arXiv2026

Autoregressive Modelling and Synthetic Generation of High-Fidelity, Statistically Equivalent 3D Microstructures for As-Manufactured Misalignments in Fiber-Reinforced Composites

This study presents an integrated framework for processing, modelling, and generating statistically representative three-dimensional fiber microstructures from experimental X-ray-$\mu$CT observations. First, an analytical slice-segment ellipse-intersection method is introduced to extract per-slice and per-fiber in-plane and out-of-plane misalignment profiles along the fiber depth. These descriptors are then used to construct a stochastic model that captures slice-wise misalignment distributions and their depth-wise evolution through, copula-based in-plane dependence, latent autoregressive continuity, and rare extreme-misalignment motifs. The model hyperparameters are calibrated using Bayesian optimization, achieving close agreement with the original statistical descriptors, with deviations generally below 10\%. The optimized statistical model is coupled with a physical generation strategy that begins with variable-radius fiber seeding layer and proceeds through an iterative slice-by-slice 3D growth scheme, where the statistical layer guides fiber evolution and Delaunay-based neighbourhood construction with ellipse-based contact resolution ensures non-overlapping, radius-augmented synthetic microstructures. The framework successfully generates about 2400 synthetic fibers while preserving strong statistical fidelity to the original X-ray-$\mu$CT data. The proposed pipeline provides a promising and scalable route for generating statistically equivalent, geometrically admissible, and simulation-ready fiber composite microstructures for virtual testing and analysis.

cs.CE↗
arXiv2026

FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence

Artificial Intelligence (AI)-generated images have become increasingly realistic and readily adaptable to concrete real-world claims, creating new challenges for verifying visual evidence. A concrete emerging risk is AI-generated refund fraud, in which manipulated or synthetic images are used to support claims about damaged products, poor delivery conditions, or service-related defects. Existing AI-generated image detection benchmarks mainly evaluate standalone authenticity classification, cross-generator transfer, or forensic localization, leaving claim-conditioned fraudulent evidence detection underexplored. To bridge this gap, we introduce FraudBench, a multimodal benchmark for detecting AI-generated fraudulent refund evidence. FraudBench is constructed from real-world user-review evidence across e-commerce, food delivery, and travel-service scenarios. We curate real evidence images together with their associated review and product metadata, identify genuine damaged and undamaged evidence through MLLM-assisted filtering and human annotation, and synthesize fake-damaged evidence from genuine undamaged reference images using six state-of-the-art image editing and generation models. Using FraudBench, we evaluate MLLMs, specialized AI-generated image detectors, and human participants under the same settings. Experiments show that current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates (TPR) far below the 50% baseline on most generator subsets. Specialized detectors generally perform better but remain inconsistent across generators and can produce false positives on real-damaged samples, revealing a clear gap between generic AI image detection and reliable claim-conditioned refund-evidence verification.

cs.CV↗
arXiv2026

A Shell-to-Shell Cohesive Line Element for Efficient Modeling of Interfacial Cracking in Overmolded Stiffened Panels

The growing use of thermoplastics in lightweight structures requires efficient numerical methods to predict debonding in overmolded parts. In this work, a novel structural cohesive element is proposed as an efficient alternative to conventional cohesive elements for modeling debonding in thermoplastic composite panels with overmolded stiffeners. Three-node, higher-order hybrid/mixed shell elements based on the Kirchhoff hypothesis are used to model thin panels and stiffeners. The novel kinematics allows to obtain the jump vector at any point over the cohesive surface from the shell displacement approximations evaluated at the element edges. The weakly enforced higher-order continuity in skin and stiffener displacements enables debonding analysis on coarse meshes. The framework is suitable for analyzing debonding in skin-stiffener structures with non-constant damage through the stiffener thickness. The model is verified for mode I, mode II and mixed-mode benchmark problems. A debonding problem is analyzed with both standard 3D cohesive elements and the proposed element. The results show that the element size in the proposed models can be much larger than that in the standard model, with more than 95% reduction in CPU time. The debonding analysis of a complex stiffened panel is also presented to demonstrate the intended use of the proposed element for simulating debonding in structural components.

cs.CE↗
arXiv2026

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks

Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-preserving alternative to data-driven surrogate modelling in scientific machine learning. However, existing realizations remain largely confined to classical AI hardware and do not directly connect to quantum structured operator design. To bridge this gap, we introduce a \emph{Quantum Neural Physics} framework and develop a Hybrid Quantum-Classical CNN Multigrid Solver (HQC-CNNMG). The proposed method maps analytically prescribed stencil operators to local quantum convolutional primitives and embeds them within a classical multilevel W-cycle architecture, combining the operator-centric view of scientific ML with the numerical rigor of multigrid solvers. Using amplitude encoding together with the Linear Combination of Unitaries (LCU) and the Quantum Fourier Transform (QFT), the resulting local quantum operators admit logarithmic-depth implementation, with circuit depth scaling as $\mathcal{O}(\log K)$ for an encoded block of size $K$ under the idealized parallel circuit model considered here. Numerical experiments on Poisson, transient diffusion, convection--diffusion, and incompressible Navier--Stokes problems demonstrate numerical consistency, stable multilevel behaviour, and workflow-level feasibility on noiseless simulators. Comparisons with representative quantum linear solver paradigms further show that the main strength of HQC-CNNMG lies in its balanced trade-off among local circuit depth, numerical robustness, and compatibility with PDE structure, rather than in fully quantum global inversion.

quant-ph↗
arXiv2026

NeuralFVM: Neural-physics-based Finite Volume Method for Turbulent Flows Using the $k$-$\omega$ Model

In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard $k$-$\omega$ model designed for efficient Graphics Processing Unit (GPU) execution. The governing equations for fluid flow and heat transfer are reformulated as local tensor operations using convolution-based stencil operators, which enables compatibility with deep learning libraries while preserving the conservative properties of the FVM. A key challenge in implementing the turbulent model within such a framework is the treatment of the stiff destruction terms in the $k$ and $\omega$ transport equations. To address this issue, an operator-splitting strategy is introduced in which the stiff destruction terms are handled semi-implicitly while the remaining terms are advanced explicitly. This formulation avoids global matrix assembly and allows the entire solver to be implemented using local tensor operations. In addition, the pressure-velocity coupling is solved using a convolution-based geometric multigrid algorithm embedded within a neural network architecture. The resulting NeuralFVM solver is validated through comparison with simulations conducted using the commercial CFD software ANSYS Fluent for several channel-flow configurations and an indoor airflow scenario. The results demonstrate close agreement in velocity, temperature, and turbulence quantities, confirming the accuracy of the proposed approach. The developed GPU framework achieves a speedup of around 19-46 times compared with its Central Processing Unit (CPU) counterpart under different meshes. Moreover, the proposed solver naturally integrates with machine learning workflows, providing a promising foundation for future data-driven turbulence modeling and optimization.

physics.flu-dyn↗
arXiv2025

A Meta-Complexity Characterization of Minimal Quantum Cryptography

We give a meta-complexity characterization of EFI pairs, which are considered the "minimal" primitive in quantum cryptography (and are equivalent to quantum commitments). More precisely, we show that the existence of EFI pairs is equivalent to the following: there exists a non-uniformly samplable distribution over pure states such that the problem of estimating a certain Kolmogorov-like complexity measure is hard given a single copy. A key technical step in our proof, which may be of independent interest, is to show that the existence of EFI pairs is equivalent to the existence of non-uniform single-copy secure pseudorandom state generators (nu 1-PRS). As a corollary, we get an alternative, arguably simpler, construction of a universal EFI pair.

quant-ph↗
arXiv2025

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques

Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift-accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques -- parallel, shared, split, and hybrid -- and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using the im2col transformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.

eess.SP↗
arXiv2025

Numerical modelling of a partially loaded intermodal container freight train passing through a tunnel

The bluff nature of a freight train locomotive, coupled with large gaps created between different wagon formations and loaded goods, influence the overall pressure wave pattern generated as the train passes through a tunnel. Typically, 1D models are used to predict the patterns and properties of tunnel pressure wave formations. However, accurate modelling of regions of separation at the head of the blunted containers and at unloaded gap sections is essential for precise predictions of pressure magnitudes. This has traditionally been difficult to capture with 1D models. Furthermore, achieving this accuracy through 3D computational methods demands exceptional mesh quality, significant computational resources, and the careful selection of numerical models. This paper evaluates various numerical models to capture these complexities within regions of flow separation. Findings have supported the development of a new 1D programme to calculate the pressure wave generated by a freight locomotive entering a tunnel, and is here further extended to consider the discontinuities of the train body created by intermodal container loading patterns, by implementing new mesh system and boundary conditions into the 1D programme. A parameterisation study for different loading configurations is also presented to improve the overall programme adaptability, and the relationship between predetermined parameters and gap length is investigated. We validate the effectiveness of the improved 1D model through comprehensive Large Eddy Simulation (LES) results and conduct an extensive parameterisation study to enhance its applicability across various loading configurations. Consequently, this research bridges the gap in freight train tunnel aerodynamics, offering a versatile 1D numerical tool for accurate pressure wave prediction.

physics.flu-dyn↗
arXiv2025

Noise Analysis and Hierarchical Adaptive Body State Estimator For Biped Robot Walking With ESVC Foot

The ESVC(Ellipse-based Segmental Varying Curvature) foot, a robot foot design inspired by the rollover shape of the human foot, significantly enhances the energy efficiency of the robot walking gait. However, due to the tilt of the supporting leg, the error of the contact model are amplified, making robot state estimation more challenging. Therefore, this paper focuses on the noise analysis and state estimation for robot walking with the ESVC foot. First, through physical robot experiments, we investigate the effect of the ESVC foot on robot measurement noise and process noise. and a noise-time regression model using sliding window strategy is developed. Then, a hierarchical adaptive state estimator for biped robots with the ESVC foot is proposed. The state estimator consists of two stages: pre-estimation and post-estimation. In the pre-estimation stage, a data fusion-based estimation is employed to process the sensory data. During post-estimation, the acceleration of center of mass is first estimated, and then the noise covariance matrices are adjusted based on the regression model. Following that, an EKF(Extended Kalman Filter) based approach is applied to estimate the centroid state during robot walking. Physical experiments demonstrate that the proposed adaptive state estimator for biped robot walking with the ESVC foot not only provides higher precision than both EKF and Adaptive EKF, but also converges faster under varying noise conditions.

cs.RO↗