A Survey of Quantum Machine Learning: Understanding the Current Landscape and Future Opportunities

Kavita R. Singh (), Sagarkumar S. Badhiye (), Kapil Gupta (), Pravinkumar M. Sonsare (), Roshni S. Khedgaonkar () and Mukesh M. Raghuwanshi ()
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Kavita R. Singh: Yeshwantrao Chavan College of Engineering, Department of Computer Technology
Sagarkumar S. Badhiye: Symbiosis International (Deemed University), Symbiosis Institute of Technology, Nagpur Campus
Kapil Gupta: St. Vincent Pallotti College of Engineering and Technology, Department of Computer Engineering
Pravinkumar M. Sonsare: Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, Department of Computer Science and Engineering
Roshni S. Khedgaonkar: Yeshwantrao Chavan College of Engineering, Department of Computer Technology
Mukesh M. Raghuwanshi: Ex-Professor and Independent Researcher Computer Science

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-49

Abstract: Abstract Quantum machine learning (QML) has emerged as a captivating integration of two cutting-edge research domains: classical and quantum computing, along with machine learning. It delves into how insights and methodologies from one field can be leveraged to address challenges in another. This review work provides a systematic review of QML. This work contributes to the sector of QML by offering a detailed overview of its principles, models, and applications, advancing the research of QML, recognizing the complexities and opportunities, showcasing potential applications in fields such as image recognition and finance, and driving future research directions. By exploring the integration of machine learning and quantum computing, this work highlights the potential impact of QML and encourages further development in this sector, highly paving the way for future applications and innovations. Initially, this work concentrates on summarizing the current techniques and trends, recognizing the research gaps and challenges, and outlining the future directions for QML development. This review gives a comprehensive overview of QML basics, models, and applications and highlights the merits and demerits of existing QML algorithms. It also addresses the future gaps and challenges in QML research and development, facilitating understanding and advancement of QML by giving a detailed evaluation of the current state and the prospects of the field. The future trends include the novel quantum algorithm’s exploration for the multimodal data, the implementation of highly robust QML approaches, and the QML’s application to real-world issues in climate modeling, finance, and healthcare. Moreover, the combination of interpretability and explainability models in the QML techniques is significant for their adoption in the high-stakes sectors. By resolving these trends and gaps, it is simple to unlock the potential of multimodal learning and QML, driving the advancements and innovation in AI.

Keywords: Quantum machine learning; Quantum computing; Chronological review; Implementation tools; Algorithms classification; Problem solved through quantum machine learning; Future research trends (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00569-z

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