发表机构
Laboratory for Advanced Computing and Intelligence Engineering; Institute of Automation, Chinese Academy of Sciences（先进计算与智能工程实验室; 中国科学院自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有QMC框架专用化、基于语言模型的智能体延迟过高的问题，提出QMClaw通用QMC框架，以RuleEngine为核心，结合语言模型，经实验验证可用于大规模量子比特测量控制场景
AI 中文摘要
随着量子计算规模不断扩大，量子测量与控制（QMC）日益受到校准工作流复杂性以及低延迟执行、健壮异常处理和可追溯工作流治理等要求的限制。现有的QMC框架是专用且针对特定任务的，而基于语言模型的QMC智能体则存在延迟过高的问题，无法满足大规模量子系统严格的时序和控制密度需求。本文提出QMClaw，一种通用的、面向工作流的QMC框架，其具有本地优先、工具治理、健壮的架构。其核心是一个以RuleEngine为中心的控制层，该控制层处理结构化上下文、执行基于规则的状态转换，并为典型的校准工作流生成执行计划。语言模型仅用于自然语言交互、高级任务理解和异常支持，从而保持关键快速路径的效率。我们使用真实量子设备数据集实现了单量子比特调优工作流作为演示和验证。我们还证明，该框架在资源成本、LLM调用次数和决策延迟方面达到了定量可接受的水平，使其能够实际部署在大规模量子比特测量与控制场景中。本研究提出了一种通用的面向工作流的QMC框架，并提供证据表明，以规则为中心的架构是可扩展量子系统校准的有前景的设计选择。
英文摘要
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.
