Quantum State Preparation via Large-Language-Model-Driven Evolution

• URL: http://arxiv.org/abs/2505.06347v1
• Date: Fri, 09 May 2025 18:00:02 GMT
• Title: Quantum State Preparation via Large-Language-Model-Driven Evolution
• Authors: Qing-Hong Cao, Zong-Yue Hou, Ying-Ying Li, Xiaohui Liu, Zhuo-Yang Song, Liang-Qi Zhang, Shutao Zhang, Ke Zhao,
• Abstract summary: We propose an automated framework for quantum circuit design to overcome the rigidity, scalability limitations, and expert dependence of traditional ones in variational quantum algorithms.<n>Our approach autonomously discovers hardware-efficient ans"atze with new features of scalability and system-size-independent number of variational parameters entirely from scratch.
• Score: 9.94808160501406
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ans\"atze 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.

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