Quantum Architecture Search: A Survey

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.

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Cited by in corpus (5)

• Topology-Driven Quantum Architecture Search Framework
• Quantum data encoding as a distinct abstraction layer in the design of quantum circuits
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
• Generative flow-based warm start of the variational quantum eigensolver
• Auto Quantum Machine Learning for Multisource Classification