用大模型自动发现可扩展的量子态制备电路
Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery
- 用大语言模型辅助设计量子电路，实现大规模系统态制备
- 在1+1维自旋链中发现4参数电路，能量偏差低于1%
- 首次构建2+1维标量场理论的可扩展浅层变分电路
高效量子态准备仍是第一性原理量子场论动力学模拟的核心挑战，因希尔伯特空间本质上是无限维的。本文提出一种大语言模型（LLM）辅助的量子电路设计框架，可系统扩展至大晶格体积。应用于1+1维XY自旋链时，该框架自主发现一个紧凑的4参数电路，能以亚百分比能量偏差捕捉边界诱导的对称性破缺，并在 exttt{Zuchongzhi}量子处理器上成功验证。基于此洞察，我们将其拓展至2+1维量子场论，此前可扩展变分变体仍难以实现。对于标量场理论，搜索得到一个对称性保持的3参数浅层电路，其优化参数在晶格尺寸n ≥ 4时收敛为与尺寸无关的常数，据我们所知，这是该类2+1维模型首个可扩展的变分电路。结果确立了人工智能辅助、人类引导的量子模拟新路径。
原文摘要 · Abstract (English)
Efficient quantum state preparation remains a central challenge in first-principles quantum simulations of dynamics in quantum field theories, where the Hilbert space is intrinsically infinite-dimensional. Here, we introduce a large language model (LLM)-assisted framework for quantum-circuit design that systematically scales state-preparation circuits to large lattice volumes. Applied to a 1+1d XY spin chain, the LLM autonomously discovers a compact 4-parameter circuit that captures boundary-induced symmetry breaking with sub-percent energy deviation, enabling successful validation on the \texttt{Zuchongzhi} quantum processor. Guided by this insight, we extend the framework to 2+1d quantum field theories, where scalable variational ansätze have remained elusive. For a scalar field theory, the search yields a symmetry-preserving, 3-parameter shallow-depth ansatz whose optimized parameters converge to size-independent constants for lattices $n \ge 4$, providing, to our knowledge, the first scalable ansatz for this class of 2+1d models. Our results establish a practical route toward AI-assisted, human-guided discovery in quantum simulation.
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