LLM 智能体工作流自动发现腔-QED 实验的物理模型
Discovering Models for Experimental Quantum Systems with Agentic AI
用 LLM 智能体去啃腔-QED 实验数据，自动提 Hamiltonian 假设再算分，3 个真实数据集验证可行，搞量子的朋友可以看看这套流程怎么搭。
一个基于 LLM 的智能体框架在实验量子光学中做模型发现：智能体提出候选 Hamiltonian 与 Lindbladian 模型、生成计算工具并定量比较竞争假设。该工作流被应用于 3 个已发表的色心腔-QED 数据集，分别来自金刚石和碳化硅中的色心。智能体成功识别出系统物理模型并刻画了色心间的无序。复现性评估显示，运行间差异主要来自研究计划生成环节，计算分析本身则较为一致。
Discovering Models for Experimental Quantum Systems with Agentic AI
A central obstacle in realizing color center cavity-QED-based quantum technologies is inhomogeneity, or disorder, among individual emitters. As the number of emitters increases, so does the complexity of the experimental data, making it difficult to quantify the present physical mechanisms of disorder. Here we introduce an artificial intelligence-driven model discovery framework in which a large language model (LLM)-based agent, equipped with tools and a structured workflow, proposes candidate Hamiltonian and Lindbladian models, generates computational tools, and quantitatively evaluates competing physical hypotheses. We apply the workflow to three previously published experimental cavity-QED datasets collected from color centers in diamond and silicon carbide. Across the experiments, the agent successfully identifies physical models for the system and characterizes the disorder. In assessing reproducibility, we find that run-to-run variability is concentrated primarily in the research plan generation step, while the computational analysis itself is substantially more consistent. Our results suggest that structured LLM agent workflows can support model discovery from quantum optical experimental data while simultaneously allowing for human expert supervision and collaboration.