用遗传算法生成更高效的量子电路，提升量子优势表现。
Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming
- 将量子优势指标融入遗传算法适应度函数，引导电路演化。
- 在伯恩斯坦-瓦兹拉尼和无结构搜索问题上加速收敛，结果媲美专家设计。
- 适合对自动化量子算法设计感兴趣的开发者与研究人员。
设计能发挥量子优势的高效量子电路已成为关键挑战。遗传算法通过人工演化展现生成此类电路的潜力，但如何将其量子优势纳入适应度函数仍属空白。本文提出两种新方法，将量子优势度量嵌入遗传算法的适应度函数中，以提升电路设计效率。实验基于伯恩斯坦-瓦兹拉尼问题与无结构数据库搜索问题进行评估。结果表明，所提方法不仅显著加快遗传算法收敛速度，且生成的电路性能可媲美专家设计的方案。研究证实，融合量子优势度量的自动化量子电路设计具有广阔前景，有望加速量子算法研发进程。
原文摘要 · Abstract (English)
Designing efficient quantum circuits that leverage quantum advantage compared to classical computing has become increasingly critical. Genetic algorithms have shown potential in generating such circuits through artificial evolution. However, integrating quantum advantage into the fitness function of these algorithms remains unexplored. In this paper, we aim to enhance the efficiency of quantum circuit design by proposing two novel approaches for incorporating quantum advantage metrics into the fitness function of genetic algorithms.1 We evaluate our approaches based on the Bernstein-Vazirani Problem and the Unstructured Database Search Problem as test cases. The results demonstrate that our approaches not only improve the convergence speed of the genetic algorithm but also produce circuits comparable to expert-designed solutions. Our findings suggest that automated quantum circuit design using genetic algorithms that incorporate a measure of quantum advantage is a promising approach to accelerating the development of quantum algorithms.
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