LLM-based Multi-Agent Copilot for Quantum Sensor

2025-08-07

AI Summary

High interdisciplinary knowledge barriers and complex optimization hinder quantum sensor development. Method: This paper proposes a large language model (LLM)-based multi-agent collaborative framework integrating external knowledge retrieval, few-shot prompting, vectorized knowledge bases, active learning, and uncertainty quantification to automate quantum system modeling, parameter optimization, and fault diagnosis. The framework enables dynamic modeling and autonomous identification of anomalous parameters. Contribution/Results: Applied to atomic cooling experiments, it autonomously generated over 10⁸ sub-microkelvin cold atoms within hours—improving efficiency by ~100×—and precisely localized multi-parameter coupled anomalies without human intervention. This work represents the first deep integration of an LLM multi-agent paradigm into the closed-loop hardware development pipeline for quantum systems, establishing a scalable, AI-driven methodology for quantum experiment automation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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${}^{ m{8}}$ sub-$ mμ$K atoms without any human intervention within a few hours, representing $sim$100$ imes$ 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.

Problem

• Overcoming interdisciplinary knowledge barriers in quantum sensor development
• Automating complex optimization processes for quantum sensor design
• Enabling autonomous anomaly detection in multi-parameter quantum experiments

Innovation

• LLM-based multi-agent framework for quantum sensors
• Integrates active learning and uncertainty quantification
• Automates modeling analysis and problem diagnosis