通过遗传编程将量子优势纳入量子电路生成
Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming
AI总结：
针对遗传算法生成量子电路时适应度函数未纳入量子优势的问题，提出两种整合量子优势指标的新方法，并在 Bernstein-Vazirani 与非结构化数据库搜索问题上验证其可加快收敛并生成专家级电路。
摘要：
设计能够利用相对于经典计算的量子优势的高效量子电路，已变得愈发关键。遗传算法已显示出通过人工演化生成此类电路的潜力。然而，将量子优势整合到这些算法的适应度函数中仍未得到探索。本文中，我们旨在通过提出两种将量子优势指标纳入遗传算法适应度函数的新方法，提高量子电路设计的效率。我们以 Bernstein-Vazirani 问题和非结构化数据库搜索问题作为测试用例评估所提方法。结果表明，我们的方法不仅提高了遗传算法的收敛速度，而且生成的电路可与专家设计的解决方案相媲美。研究结果表明，使用纳入量子优势度量的遗传算法进行自动化量子电路设计，是加速量子算法开发的一种有前景的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
