The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry. In image classification benchmarks, the best generated feature map outperforms representative quantum feature maps and, when scaled to larger qubit counts, surpasses the classical radial basis function kernel. In molecular ground state estimation across seven molecules, the generated ansatz attains competitive accuracy with widely used chemically inspired and hardware-efficient constructions while satisfying the imposed scaling constraints. These results establish LLM driven agentic system as a viable paradigm for automated quantum circuit design and illustrate how AI systems can participate in iterative scientific optimization workflows across scientific domains.

翻译：高性能量子电路的设计在很大程度上仍然依赖于人类专业知识。我们提出了一种自主智能体框架，该框架利用大语言模型（LLMs）在明确的设计约束条件下进行迭代式量子电路设计。我们的系统集成了七个组件：探索、生成、讨论、验证、存储、评估和审查。这些组件形成了一个闭环工作流，将基于网页的知识获取、基于文献的批判、可执行代码生成和实验反馈相结合。我们在两个任务上评估了该框架：量子机器学习中的量子特征映射构建，以及量子化学中变分量子本征求解器应用的初始态生成。在图像分类基准测试中，生成的最佳特征映射优于代表性量子特征映射，并且当扩展到更多量子比特时，超越了经典径向基函数核。在跨七个分子的分子基态估计中，生成的初始态在满足施加的缩放约束条件下，达到了与广泛使用的化学启发式和硬件高效构造相媲美的精度。这些结果确立了由大语言模型驱动的智能体系统作为自动化量子电路设计的可行范式，并展示了人工智能系统如何跨科学领域参与迭代式科学优化工作流。