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Weilong Wang

Publications and source records attributed to Weilong Wang.

At least 19 recordsLinked to original sources
arXiv2026

Logarithmic Bias of Zero-Noise Extrapolation under 1/f Noise

Zero-noise extrapolation estimates ideal quantum observables by amplifying noise and extrapolating the measured response. Its accuracy depends on whether the extrapolation model captures the physical effect of amplification, including correlations between repeated operations. Here we derive the response of complete multiqubit gate folding in the simultaneous presence of time-local Markovian noise and stationary finite-band 1/f noise. An ordered circuit kernel couples the observable to noise memory; exact block counting shows how folding samples correlations at increasing time separations. The resulting response contains an r^2 ln r coordinate, with a coefficient fixed by the noise spectrum and the integrated forward-inverse control response. Ordinary polynomial Richardson extrapolation leaves a calculable contribution from this coordinate; the three-node rule (1,3,5) retains 2.73 theta. We construct response-matched extrapolation, including a log-domain implementation, that cancels this contribution at the retained order. Explicit bounds separate the remaining control-shape, finite-band, higher-order, and native-offset errors. The theory applies to arbitrary finite multiqubit circuits within the stated control and noise model, and distinguishes removal of memory-induced bias from an unconditional guarantee of the ideal intercept. Single- and ten-qubit numerical examples illustrate the predicted correction and its response dependence.

quant-ph↗
arXiv2026

Reconfigurable Bus-based Quantum Router for Modular Superconducting Processors

Scaling superconducting quantum processors requires interconnects that provide both non-local connectivity and parallel entangling operations. While current quantum routers offer greater connectivity than nearest-neighbour topologies, their support for parallel, independently addressable two-qubit gates remains fundamentally limited, imposing severe compilation overheads that constitute a critical bottleneck. Here we introduce a bus-based reconfigurable quantum router that enables parallel controlled-$Z$ (CZ) gates for modular superconducting processors. Constructed from a flux-tunable SQUID network, the router selectively connects interface qubits to two shared buses, allowing destructive interference to suppress idle interactions while supporting two disjoint CZ gates in parallel. Full-system Hamiltonian simulations yield parallel-gate infidelities ranging from $7.9\times10^{-4}$ to $1.8\times10^{-3}$ under the operating conditions considered, and an open-system analysis identifies the coherence requirements for high-fidelity operation. We further assess the circuit-level consequences using hardware-aware compilation and resource-constrained scheduling. For the 36-qubit quantum Fourier transform (QFT), QAOA-MaxCut, and random-pairing circuits, the router reduces the median SWAP count by up to $34\%$ and the native CZ count by up to $20\%$ relative to a matched two-dimensional grid. Circuit-depth reductions are workload-dependent, reaching $20\%$ for QAOA-MaxCut but remaining negligible for the QFT despite its lower gate count. These results show that enhanced connectivity and schedulable parallelism provide complementary benefits for connectivity-intensive algorithms, establishing the router as a compiler-visible hardware resource and providing a scalable architectural pathway toward constructing highly connected modular superconducting quantum processors.

quant-ph↗
arXiv2026

QMClaw: A Scalable General-purpose Framework for Quantum Measurement and Control

As quantum computing continues to scale, quantum measurement and control (QMC) are increasingly constrained by calibration workflow complexity and by requirements for low-latency execution, robust exception handling, and traceable workflow governance. Existing frameworks for QMC are specialized and task-specific, while language-model-based agents for QMC suffer from excessive latency and cannot satisfy the strict timing and control-density demands of large-scale quantum systems. Here we propose QMClaw, a general, workflow-oriented framework for QMC built, featuring a local-first, tool-governed, robust architecture. At its core is a RuleEngine-centered control layer that processes structured context, performs rule-based state transitions, and generates execution plans for typical calibration workflows. Language models are used only for natural-language interaction, high-level task understanding, and exception support, keeping the critical fast path efficient. We implement a single qubit tune-up workflow as a demonstration and validation using real quantum device dataset. We also prove that the framework achieves quantitatively acceptable levels in terms of resource cost, LLM calling times and decision latency, enabling its practical deployment in large-scale quantum qubit measurement and control scenarios. This work presents a general workflow-oriented framework for QMC and provides evidence that rule-centered architectures are a promising design choice for scalable quantum-system calibration.

quant-ph↗
arXiv2026

One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread

Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-making, these indicators are embedded in information spread: users see both an AI prediction and earlier judgments shaped by the same AI, and their own judgments may then enter the public history. Yet how to analytically characterize this process remains under-explored. We therefore introduce a social-learning lens for this setting by extending the classical Bayesian cascade model with the AI indicator as a shared public signal. The resulting Gateway condition compares the evidence from the AI prediction with users' private impressions. Through this view, we show that AI changes what public history means. Crowd agreement may reflect accumulated independent human evidence, or repeated dependence on the same AI prediction. This creates a preservation-correction trade-off: stronger reliance on AI can preserve correct predictions, but can also lock in incorrect ones by blocking corrective private impressions. We calibrate the model using human-subject data on news veracity judgments. Although the AI outperforms human users, the average user weights it below her own impression but above several peer judgments, while individual users vary from discounting the AI to relying on it enough to cascade. Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative. We conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.

cs.HC↗
arXiv2026

Separate Control of Transient Leakage Exposure and Endpoint Leakage in Fast Transmon Gates

Leakage to noncomputational states limits the speed of single-qubit gates in weakly anharmonic transmons. Conventional pulse-shaping methods, including derivative removal by adiabatic gate (DRAG), primarily suppress the leakage remaining at the end of a gate. Here we demonstrate that endpoint leakage and the transient leakage population that accumulates during the gate represent distinct control objectives. Endpoint leakage is associated with the drive spectrum at the anharmonicity, whereas transient exposure depends on spectral weight over a finite frequency band and governs the additional leakage induced by dephasing. A spectral null at the leakage transition therefore suppresses the endpoint amplitude without necessarily reducing transient exposure. Based on this distinction, we introduce a path--endpoint separation pulse that combines transient-path shaping with a two-tone endpoint correction. For a $10$ ns $R_X(π/2)$ gate with an anharmonicity magnitude of $0.2$ GHz, numerical simulations show a $21\%$ reduction in transient exposure relative to cosine DRAG and a corresponding $20\%$ reduction in dephasing-induced excess leakage. The two correction tones further suppress residual leakage through the $|2\rangle$ and $|3\rangle$ channels, lowering the coherent endpoint leakage from approximately $7\times10^{-7}$ to $3\times10^{-8}$ without increasing transient exposure. These results establish transient exposure and endpoint leakage as complementary targets for the design of fast transmon gates.

quant-ph↗
arXiv2026

Tunable Nonlocal $ZZ$ Interaction for Remote Controlled-Z Gates Between Distributed Fixed-Frequency Qubits

Scaling superconducting quantum processors toward fault-tolerant operation will likely require architectures that extend beyond monolithic chips. Modular processors connected by low-loss superconducting links provide a promising route, but implementing entangling gates between remote fixed-frequency qubits remains challenging. Here we propose a distributed architecture in which two synchronously controlled double-transmon couplers mediate the interaction between fixed-frequency transmons in separate packages connected by a 25-cm coaxial cable. The scheme activates a tunable nonlocal $ZZ$ interaction on demand while suppressing residual static coupling, allowing the superconducting link to function as a gate-native interconnect rather than solely as a state-transfer channel. Circuit-level simulations show an on/off ratio exceeding $10^6$ and a remote controlled-Z gate with a projected coherent fidelity of $99.99\%$ under experimentally relevant parameters. Open-system simulations further indicate that, within the representative Markovian noise model considered here, endpoint-qubit decoherence is the largest contribution to gate infidelity, while photon loss in the retained cable modes remains smaller but non-negligible. These results identify DTC-mediated tunable nonlocal coupling as a promising gate primitive for modular superconducting processors based on fixed-frequency qubits.

quant-ph↗
arXiv2026

Time-frequency-correlated Native CCZ Gate in Superconducting Circuits

Practical quantum advantage hinges on executing deep quantum circuits within the coherence limits of noisy intermediate-scale quantum processors. The absence of native, high-fidelity multi-qubit gates remains a major bottleneck, as their decomposition into single- and two-qubit gates leads to prohibitive depth and error overhead. Here, we propose a hardware-efficient protocol that directly implements a native controlled-controlled-Z (CCZ) gate in a tunable-coupler superconducting circuit. Our theoretical protocol activates a resonant three-qubit interaction via a time-frequency correlated virtual process, explicitly relying on the dynamic resonant exchange within the $|101\rangle \leftrightarrow |020\rangle$ transition manifold. This approach is compatible with standard tunable-coupler architectures without requiring additional control resources. Through a systematic calibration workflow combining pulse shaping and active cancellation of residual phases, we demonstrate a gate fidelity exceeding 99\% within $165\,\mathrm{ns}$ -- significantly outperforming decomposed sequences. Comprehensive error budgeting confirms that the gate performance remains robust against realistic experimental imperfections. Furthermore, we show that this scheme can be naturally extended to a continuous $\mathrm{CCPhase}(θ)$ gate set. This work provides a direct, high-fidelity route to three-qubit entanglement, offering promising prospects for efficient execution of quantum algorithms on near-term superconducting hardware.

quant-ph↗
arXiv2026

Raw-Curve Quantum Fingerprints: A Mahalanobis Authentication Framework with Drift Early Warning and Adversarial Detection

Quantum cloud platforms are poised to deliver powerful computing capabilities, but users have no direct means to verify which physical device executes their workload. This lack of transparency enables hardware substitution attacks, where a malicious adversary could redirect a job to a substituted or inferior processor. We present a general authentication framework that addresses this problem by constructing multi-dimensional quantum fingerprints from raw measurement data. Without any curve fitting, we directly concatenate the raw statistics of complementary experiments into a high-dimensional feature vector that preserves subtle device-specific information. A Mahalanobis nearest-neighbor classifier achieves 100\% benign authentication accuracy on three superconducting processors over a three-week chronological split. The classifier naturally yields an authentication confidence $C_{\mathrm{claimed}}$ which reveals device-specific safety margins and motivates per-device alert thresholds. We assess the framework's robustness under two distinct scenarios. Under additive isotropic Gaussian noise, $C_{\mathrm{claimed}}$ decays predictably at a rate explained by inverse covariance traces, enabling an early warning mechanism. Against white-box adversarial perturbations, the same confidence threshold detects $L_2$ targeted attacks with near-perfect success and reveals device-dependent empirical thresholds for $L_\infty$ attacks, while untargeted and sparse attacks are ineffective. The proposed framework thus unifies fingerprint extraction, drift-resilient authentication, proactive health monitoring, and adversarial defense, offering a practical step toward trustworthy quantum cloud computing.

quant-ph↗
arXiv2026

Gram-Anchored Prompt Learning for Vision-Language Models via Second-Order Statistics

Parameter-efficient prompt learning has become the de facto standard for adapting Vision-Language Models (VLMs) to downstream tasks. Existing approaches predominantly focus on aligning text prompts with first-order visual features (i.e., spatial feature maps). While effective for fine-grained semantic discrimination, we argue that relying solely on first-order information is insufficient for robust adaptation, as these spatially entangled features are highly susceptible to domain shifts and local noise. In this work, we propose \textbf{Gram-Anchored Prompt Learning (GAPL)} for Vision-Language Models via Second-Order Statistics, a framework that synergizes local semantic alignment with global structural consistency. Methodologically, we introduce an additional second-order statistical stream via \textbf{Gram matrices} that augments the standard first-order spatial interaction. By anchoring prompts to these second-order priors, our approach enables language representations to dynamically adapt to statistical distribution shifts across diverse domains. Extensive experiments indicate the effectiveness of the second-order features, and show compelling performances of GAPL on various benchmarks.

cs.CV↗
arXiv2026

Fast CZ Gate via Energy-Level Engineering in Superconducting Qubits with a Tunable Coupler

In superconducting quantum circuits, decoherence errors in qubits constitute a critical factor limiting quantum gate performance. To mitigate decoherence-induced gate infidelity, rapid implementation of quantum gates is essential. Here we propose a scheme for rapid controlled-Z (CZ) gate implementation through energy-level engineering, which leverages Rabi oscillations between the $\left|11\right\rangle$ state and the non-computational state in a tunable-coupler architecture. Numerical simulations achieved a $\mathrm{22~ns}$ nonadiabatic CZ gate with fidelity over $99.99\%$. We further investigated the performance of the CZ gate in the presence of anharmonicity offsets. The results demonstrate that a high-fidelity CZ gate with an error rate below $10^{-4}$ remains achievable even with finite anharmonicity variations. Furthermore, the detrimental impact of spectator qubits in different quantum states on the fidelity of CZ gate is effectively suppressed by incorporating a tunable coupler. This scheme exhibits potential for extending the circuit execution depth constrained by coherence time limitations.

quant-ph↗
arXiv2025

DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models

Vision-Language-Action (VLA) models have advanced autonomous driving, but existing benchmarks still lack scenario diversity, reliable action-level annotation, and evaluation protocols aligned with human preferences. To address these limitations, we introduce DriveAction, the first action-driven benchmark specifically designed for VLA models, comprising 16,185 QA pairs generated from 2,610 driving scenarios. DriveAction leverages real-world driving data proactively collected by drivers of autonomous vehicles to ensure broad and representative scenario coverage, offers high-level discrete action labels collected directly from drivers' actual driving operations, and implements an action-rooted tree-structured evaluation framework that explicitly links vision, language, and action tasks, supporting both comprehensive and task-specific assessment. Our experiments demonstrate that state-of-the-art vision-language models (VLMs) require both vision and language guidance for accurate action prediction: on average, accuracy drops by 3.3% without vision input, by 4.1% without language input, and by 8.0% without either. Our evaluation supports precise identification of model bottlenecks with robust and consistent results, thus providing new insights and a rigorous foundation for advancing human-like decisions in autonomous driving.

cs.CV↗
arXiv2025

BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation

Scene Graph Generation (SGG) remains a challenging task due to its compositional property. Previous approaches improve prediction efficiency through end-to-end learning. However, these methods exhibit limited performance as they assume unidirectional conditioning between entities and predicates, which restricts effective information interaction. To address this limitation, we propose a novel bidirectional conditioning factorization in a semantic-aligned space for SGG, enabling efficient and generalizable interaction between entities and predicates. Specifically, we introduce an end-to-end scene graph generation model, the Bidirectional Conditioning Transformer (BCTR), to implement this factorization. BCTR consists of two key modules. First, the Bidirectional Conditioning Generator (BCG) performs multi-stage interactive feature augmentation between entities and predicates, enabling mutual enhancement between these predictions. Second, Random Feature Alignment (RFA) is present to regularize feature space by distilling multi-modal knowledge from pre-trained models. Within this regularized feature space, BCG is feasible to capture interaction patterns across diverse relationships during training, and the learned interaction patterns can generalize to unseen but semantically related relationships during inference. Extensive experiments on Visual Genome and Open Image V6 show that BCTR achieves state-of-the-art performance on both benchmarks.

cs.CV↗
arXiv2025

EDA-Q: Electronic Design Automation for Superconducting Quantum Chip

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.

cs.ET↗
arXiv2024

A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space

Hyperbolic embeddings are a class of representation learning methods that offer competitive performances when data can be abstracted as a tree-like graph. However, in practice, learning hyperbolic embeddings of hierarchical data is difficult due to the different geometry between hyperbolic space and the Euclidean space. To address such difficulties, we first categorize three kinds of illness that harm the performance of the embeddings. Then, we develop a geometry-aware algorithm using a dilation operation and a transitive closure regularization to tackle these illnesses. We empirically validate these techniques and present a theoretical analysis of the mechanism behind the dilation operation. Experiments on synthetic and real-world datasets reveal superior performances of our algorithm.

cs.LG↗
arXiv2024

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.

cs.SI↗
arXiv2024

Discovering Universal Semantic Triggers for Text-to-Image Synthesis

Recently text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such models and their potential ethical issues have not been fully explored. In this paper, we introduce Universal Semantic Trigger, a meaningless token sequence that can be added at any location within the input text yet can induce generated images towards a preset semantic target.To thoroughly investigate it, we propose Semantic Gradient-based Search (SGS) framework. SGS automatically discovers the potential universal semantic triggers based on the given semantic targets. Furthermore, we design evaluation metrics to comprehensively evaluate semantic shift of images caused by these triggers. And our empirical analyses reveal that the mainstream open-source text-to-image models are vulnerable to our triggers, which could pose significant ethical threats. Our work contributes to a further understanding of text-to-image synthesis and helps users to automatically auditing their models before deployment.

cs.CR↗
arXiv2023

NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning

At present, backdoor attacks attract attention as they do great harm to deep learning models. The adversary poisons the training data making the model being injected with a backdoor after being trained unconsciously by victims using the poisoned dataset. In the field of text, however, existing works do not provide sufficient defense against backdoor attacks. In this paper, we propose a Noise-augmented Contrastive Learning (NCL) framework to defend against textual backdoor attacks when training models with untrustworthy data. With the aim of mitigating the mapping between triggers and the target label, we add appropriate noise perturbing possible backdoor triggers, augment the training dataset, and then pull homology samples in the feature space utilizing contrastive learning objective. Experiments demonstrate the effectiveness of our method in defending three types of textual backdoor attacks, outperforming the prior works.

cs.CR↗
arXiv2022

Factoring integers with sublinear resources on a superconducting quantum processor

Shor's algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglog N), which is sublinear in the bit length of the integer $N$, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.

quant-ph↗