LLM 进化搜索发现超越现有最优的量子电路设计
Discovering state-of-the-art quantum circuits with large language models
让 LLM 提方案、评估器打分的进化搜索，8 个量子任务里 7 个刷新了电路资源的最优记录，方法本身也值得借鉴。
研究团队提出一个进化框架：LLM 负责提出量子电路设计方案，独立评估器负责验证和打分，循环迭代。框架在数值计算、化学、优化、量子模拟和数论共 8 个任务上运行，其中 7 个任务发现的电路在 Toffoli 门数量和量子比特数量上都显著优于现有最优结果。第 8 个任务中，搜索找到一种素域模逆运算的可逆架构，使经典的 divsteps 算法首次可用于可逆整数运算，并改善了模逆运算中 Toffoli 门与量子比特的权衡。
Discovering state-of-the-art quantum circuits with large language models
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. We apply this framework to tasks spanning numerical computation, chemistry, optimization, quantum simulation, and number theory. In seven of the eight tasks, the discovered circuits significantly reduce both Toffoli and qubit counts relative to the state of the art. In the eighth, the search discovers a new reversible architecture for prime-field modular inversion. By handling reversibility differently, the architecture makes divsteps -- a classical algorithm previously impractical for reversible integer arithmetic -- viable and substantially improves the Toffoli-qubit trade-off for modular inversion. These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.