A Survey on Quantum Machine Learning: Basics, Current Trends, Challenges, Opportunities, and the Road Ahead

TMLR Paper4399 Authors

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 survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission:

We sincerely thank the editor and all reviewers for their detailed and constructive feedback. In response, we have made substantial revisions throughout the manuscript to improve its clarity, completeness, and scholarly rigor. Key algorithmic sections, including VQE and Quantum Kernels (Section 4), were expanded to include implementation choices, optimization challenges, and limitations such as barren plateaus, along with relevant mitigation strategies and foundational citations. Comparative summaries were added to Sections 4.4, 6.2, and 6.4 to highlight trade-offs between QML models, quantum hardware, and software tools. The quantum data section (Section 5) was reorganized to better distinguish classical preprocessing from quantum encoding, with expanded context on dataset use cases and challenges. Sections 2 and 3 were restructured to provide a clearer narrative of quantum principles and complexity theory, and Section 3.2 was revised to more accurately reflect the implications of quantum supremacy benchmarks. We have also clarified the current hardware constraints and resource limitations in Sections 2.6.2 and 4.8. Subsections 7.4–7.6 were revised to avoid speculative claims and emphasize concrete techniques. In Section 8, we now explicitly acknowledge the broader ethical and societal implications of QML and QC, including cryptographic risks and access disparities. These revisions collectively enhance the pedagogical value, technical depth, and relevance of our survey.

Assigned Action Editor: Christopher Mutschler

Submission Number: 4399