Large language models (LLM) have achieved remarkable outcomes in addressing complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet few works have explored the potential of LLM in quantum computing. The most challenging problem is how to leverage LLMs to automatically generate quantum circuits at a large scale. In this paper, we address such a challenge by fine-tuning LLMs and injecting the domain-specific knowledge of quantum computing. In particular, we investigate the mechanisms to generate training data sets and construct the end-to-end pipeline to fine-tune pre-trained LLMs that produce parameterized quantum circuits for optimization problems. We have prepared 14,000 quantum circuits covering a substantial part of the quantum optimization landscape: 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. The fine-tuned LLMs can construct syntactically correct parametrized quantum circuits in the most recent OpenQASM 3.0. We have evaluated the quality of the parameters by comparing them to the optimized expectation values and distributions. Our evaluation shows that the fine-tuned LLM outperforms state-of-the-art models and that the parameters are better than random. The LLM-generated parametrized circuits and initial parameters can be used as a starting point for further optimization, \emph{e.g.,} templates in quantum machine learning and the benchmark for compilers and hardware.

本研究解决了如何利用大型语言模型（LLM）自动生成量子电路这一挑战。通过微调LLMs并注入量子计算的领域知识，研究人员构建了一个完整的培训数据集及流程，成功生成了14,000个参数化量子电路，从而在量子优化问题中表现出优异的性能和更好的参数质量。这一方法不仅提高了电路生成的效率，也为后续的量子机器学习和量子编译器等应用奠定了基础。

针对电路生成的量子优化问题对大型语言模型进行微调