标题： 监督量子机器学习：从量子位到企业应用的未来展望 显示英文标题

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

摘要： 监督量子机器学习（QML）是量子计算和经典机器学习的交叉领域，旨在利用量子资源来支持模型训练和推理。 本文回顾了监督QML的最新进展，重点介绍了变分量子电路、量子神经网络和量子核方法等方法，以及混合量子-经典工作流程。 我们考察了最近的实验研究，这些研究显示了量子优势的部分迹象，并描述了当前的局限性，包括噪声、平坦峡谷、可扩展性问题以及缺乏对经典方法性能改进的正式证明。 主要贡献是一个十年展望（2025-2035），概述了监督QML可能的发展，包括一个路线图，描述了在未来十年内QML可能在应用研究和企业系统中使用的条件。 显示英文摘要

摘要： 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.