A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
It provides a comprehensive overview for researchers and practitioners interested in QML, but it is incremental as a survey without new experimental results.
This survey paper tackles the integration of quantum computing with machine learning to create quantum machine learning (QML) systems, reviewing algorithms, datasets, hardware, software, and applications to consolidate the current landscape and outline future opportunities and challenges.
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 survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.