LLM-based Multi-Agent Copilot for Quantum Sensor

• URL: http://arxiv.org/abs/2508.05421v1
• Date: Thu, 07 Aug 2025 14:14:08 GMT
• Title: LLM-based Multi-Agent Copilot for Quantum Sensor
• Authors: Rong Sha, Binglin Wang, Jun Yang, Xiaoxiao Ma, Chengkun Wu, Liang Yan, Chao Zhou, Jixun Liu, Guochao Wang, Shuhua Yan, Lingxiao Zhu,
• Abstract summary: QCopilot is a multi-agent framework integrating external knowledge access, active learning, and uncertainty quantification for quantum sensor design and diagnosis.<n>Applying QCopilot to atom cooling experiments, we generated 10$rm8$ sub-$rmmu$K atoms without any human intervention within a few hours.
• Score: 6.110308943823486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-$\rm{\mu}$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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