Abstract
We propose an automated framework for quantum circuit design by integrating large-language models (LLMs) with evolutionary optimization to overcome the rigidity, scalability limitations, and expert dependence of traditional ones in variational quantum algorithms. Our approach (FunSearch) autonomously discovers hardware-efficient ansätze with new features of scalability and system-size-independent number of variational parameters entirely from scratch. Demonstrations on the Ising and XY spin chains with n = 9 qubits yield circuits containing 4 parameters, achieving near-exact energy extrapolation across system sizes. Implementations on quantum hardware (Zuchongzhi chip) validate practicality, where two-qubit quantum gate noises can be effectively mitigated via zero-noise extrapolations for a spin chain system as large as 20 sites. This framework bridges algorithmic design and experimental constraints, complementing contemporary quantum architecture search frameworks to advance scalable quantum simulations.
Abstract (translated)
我们提出了一种自动化框架，通过将大型语言模型（LLM）与进化优化相结合来设计量子电路。这一方法旨在克服传统变分量子算法在灵活性、可扩展性以及对专家依赖方面的局限性。我们的方法（FunSearch）能够在没有任何先验知识的情况下自主发现硬件效率高的解空间，并且能够生成具有新特性的可扩展性和系统大小无关的变分参数数量。 在含有n=9个量子比特的伊辛模型和XY自旋链上的演示显示，这种方法可以设计出仅包含4个参数的电路，能够在不同系统规模下实现近似精确的能量外推。通过在量子硬件（如祖冲之芯片）上实施这一框架进行验证，我们展示了其实际可行性：即使是多达20位的自旋链系统中的双比特量子门噪声也可以通过零噪音外推有效缓解。 该框架将算法设计与实验约束相结合，补充了现有的量子架构搜索框架，并为实现可扩展的量子模拟提供了新的途径。
URL
https://arxiv.org/abs/2505.06347
