用大模型+多智能体自动设计量子传感器，提速百倍且无需人工干预。
LLM-based Multi-Agent Copilot for Quantum Sensor
- 构建多智能体系统，结合外部知识与主动学习，自适应优化实验流程。
- 在数小时内生成10⁸个亚微开级原子，比人工快约100倍。
- 可自主诊断多参数异常，适合量子实验自动化与科研团队协作。
大型语言模型（LLM）虽具广泛适用性，但在量子传感器开发中受限于跨学科知识壁垒及复杂的优化过程。本文提出QCopilot，一个基于LLM的多智能体框架，整合外部知识检索、主动学习与不确定性量化，用于量子传感器的设计与诊断。该框架采用商业LLM配合少样本提示工程和向量知识库，通过专用智能体动态选择优化方法、自动完成建模分析，并独立执行问题诊断。应用于原子冷却实验，仅需数小时即生成10⁸个亚微开级原子，较人工实验提速约100倍。通过持续积累先验知识并支持动态建模，QCopilot能自主识别多参数实验中的异常参数。本工作降低了大规模量子传感器部署的门槛，可推广至其他量子信息系统。
原文摘要 · Abstract (English)
Large language models (LLM) exhibit broad utility but face limitations in quantum sensor development, stemming from interdisciplinary knowledge barriers and involving complex optimization processes. Here we present QCopilot, an LLM-based multi-agent framework integrating external knowledge access, active learning, and uncertainty quantification for quantum sensor design and diagnosis. Comprising commercial LLMs with few-shot prompt engineering and vector knowledge base, QCopilot employs specialized agents to adaptively select optimization methods, automate modeling analysis, and independently perform problem diagnosis. Applying QCopilot to atom cooling experiments, we generated 10${}^{\rm{8}}$ sub-$\rmμ$K atoms without any human intervention within a few hours, representing $\sim$100$\times$ speedup over manual experimentation. Notably, by continuously accumulating prior knowledge and enabling dynamic modeling, QCopilot can autonomously identify anomalous parameters in multi-parameter experimental settings. Our work reduces barriers to large-scale quantum sensor deployment and readily extends to other quantum information systems.
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