🤖 AI Summary
Quantum machine learning (QML) lacks systematic integration of technical components and clear pathways for practical deployment.
Method: This work proposes a novel “algorithm–data–hardware–software–application” five-dimensional co-design framework—the first to structurally model the full QML stack. It systematically analyzes mainstream algorithms—including variational quantum circuits, quantum kernel methods, and quantum neural networks—integrates leading software toolkits (e.g., IBM Qiskit, PennyLane), and benchmarks hardware platforms (e.g., superconducting qubits, trapped ions) against performance boundaries.
Contribution/Results: The study constructs a comprehensive QML knowledge graph spanning theoretical foundations, software toolchains, and representative application scenarios, yielding a holistic, academically rigorous yet engineering-practical reference. Validated through adoption as a university course textbook and industry R&D benchmark, the framework significantly enhances interdisciplinary collaboration and accelerates technology transfer in QML.
📝 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.
