Quantum Architecture Search: A Survey

TL;DR

This survey reviews the emerging field of quantum architecture search (QAS), highlighting its role in automating the design of parameterized quantum circuits to overcome current challenges in quantum computing.

Contribution

It provides a comprehensive overview of QAS methods, discusses key challenges, and surveys solutions to facilitate future research in automated quantum circuit design.

Findings

QAS employs machine learning and optimization techniques.

Current challenges include hardware limitations and search complexity.

QAS has potential to accelerate quantum algorithm development.

Abstract

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.