Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

TL;DR

This paper reviews recent advances in supervised quantum machine learning, discusses current challenges, and provides a ten-year outlook on its future development and potential enterprise applications.

Contribution

It offers a comprehensive future roadmap for supervised QML from 2025 to 2035, highlighting conditions for practical adoption in research and industry.

Findings

Partial quantum advantage observed in experiments

Current limitations include noise and scalability issues

No formal proofs of performance superiority over classical methods

Abstract

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.