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A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead (2310.10315v2)

Published 16 Oct 2023 in quant-ph and cs.LG

Abstract: Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This paper serves as a valuable resource for readers seeking to understand the current state-of-the-art techniques in the QML field, offering a solid foundation to embark on further exploration and development in this rapidly evolving area.

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Authors (4)
  1. Kamila Zaman (4 papers)
  2. Alberto Marchisio (56 papers)
  3. Muhammad Abdullah Hanif (60 papers)
  4. Muhammad Shafique (204 papers)
Citations (16)
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