Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum computing to solve real-world problems is still hampered by a number of challenges, including hardware limitations and a relatively under-explored landscape of quantum algorithms, especially when compared to the extensive development of classical computing. The design of quantum circuits, in particular parameterized quantum circuits (PQCs), which contain learnable parameters optimized by classical methods, is a non-trivial and time-consuming task requiring expert knowledge. As a result, research on the automated generation of PQCs, known as quantum architecture search (QAS), has gained considerable interest. QAS focuses on the use of machine learning and optimization-driven techniques to generate PQCs tailored to specific problems and characteristics of quantum hardware. In this paper, we provide an overview of QAS methods by examining relevant research studies in the field. We discuss main challenges in designing and performing an automated search for an optimal PQC, and survey ways to address them to ease future research.

翻译：近年来，量子计算取得了显著进展，不仅在研究实验室中引起了极大兴趣，也在各行业中备受关注。然而，与经典计算的广泛发展相比，利用量子计算解决实际问题仍面临诸多挑战，包括硬件限制以及量子算法领域的相对不成熟。量子电路的设计，尤其是包含可通过经典方法优化的可学习参数化量子电路（PQC），是一项需要专业知识且耗时的非平凡任务。因此，对PQC自动化生成的研究——即量子架构搜索（QAS）——引起了广泛关注。QAS专注于利用机器学习与优化驱动的技术来生成针对特定问题及量子硬件特性定制的PQC。本文通过梳理相关领域的研究文献，概述了QAS方法。我们讨论了在设计并执行最优PQC自动搜索过程中面临的主要挑战，并总结了应对这些挑战的方法，以期为未来研究提供便利。