Quantum Architecture Search: A Survey
It addresses the problem of automating quantum circuit design for researchers and practitioners in quantum computing, but it is incremental as it surveys existing methods rather than introducing new ones.
This survey tackles the challenge of designing quantum circuits, specifically parameterized quantum circuits (PQCs), which is time-consuming and requires expert knowledge, by providing an overview of quantum architecture search (QAS) methods that use machine learning and optimization to automate this process.
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.