Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
It offers a forward-looking perspective for researchers and practitioners interested in the potential integration of quantum computing into machine learning, though it is incremental as a review and outlook paper.
This paper reviews supervised quantum machine learning (QML), highlighting methods like variational quantum circuits and quantum kernel methods, and notes experimental studies with partial indications of quantum advantage but current limitations such as noise and scalability issues. It provides a ten-year outlook (2025-2035) outlining potential developments and a roadmap for QML in applied research and enterprise systems.
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantage and describe current limitations including noise, barren plateaus, scalability issues, and the lack of formal proofs of performance improvement over classical methods. The main contribution is a ten-year outlook (2025-2035) that outlines possible developments in supervised QML, including a roadmap describing conditions under which QML may be used in applied research and enterprise systems over the next decade.