arXiv subjects

Hanshi Zhao

Publications and source records attributed to Hanshi Zhao.

3 recordsLinked to original sources

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.

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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.

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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.

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