展望2025-2035年量子机器学习在企业中的应用前景
Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
- 结合变分量子线路与混合计算流程，探索量子优势路径
- 实验显示初步量子优势迹象，但受限于噪声与可扩展性
- 提供未来十年量子机器学习落地的路线图，适合产业界参考
监督式量子机器学习（Supervised Quantum Machine Learning, QML）融合量子计算与经典机器学习，旨在利用量子资源支持模型训练与推理。本文综述了近期进展，涵盖变分量子电路、量子神经网络、量子核方法及混合量子-经典工作流。分析表明，当前实验已呈现部分量子优势迹象，但面临噪声、梯度消失（barren plateaus）、可扩展性不足以及缺乏性能优于经典方法的严格证明等挑战。主要贡献在于提出2025至2035年的十年展望，描绘了监督式QML在应用研究与企业系统中可能实现的条件与发展路径。
原文摘要 · Abstract (English)
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.
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