Quantum computing is regarded as a promising paradigm that may overcome the current computational power bottlenecks in the post-Moore era. The increasing maturity of quantum processors, especially superconducting ones, provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies such as quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, one can reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.

本文论述了量子计算作为克服后摩尔时代计算能力瓶颈的一种有前途的范式，特别是超导量子处理器的日益成熟，为量子算法的发展和实施提供了更多的可能性。此外，研究还表明相关算法的规模和精确度正在稳步提高，尤其是与人工智能方法的整合。本文系统地回顾和总结了大量的文献，探讨了从算法级到量子硬件级整合设计和优化方案的可行性，结合了逻辑电路设计和编译优化的步骤。借助人工智能算法的卓越认知和学习能力，可以减少手动设计成本，提高执行的精确度和效率，并促进量子算法在硬件上的实施和优势验证。

量子电路综合和编译优化：概述与前景