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  4. Quantum Architecture Search: A Survey
 
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June 10, 2024
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

Quantum Architecture Search: A Survey

Title Supplement
Published on arXiv
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.
Author(s)
Martyniuk, Darya  
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Jung, Johannes
Freie Univ. Berlin  
Paschke, Adrian  
Freie Universität Berlin  
DOI
10.48550/arXiv.2406.06210
Language
English
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Keyword(s)
  • Quantum architecture search

  • Quantum neural architecture circuit search

  • Automatic circuit generation

  • Variable Ansatz

  • Quantum circuit structure search

  • QAS

  • AutoM

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