教量子科研人员如何用深度学习高效探索复杂系统。
Deep Learning in Classical and Quantum Physics
- 结合理论与实践，讲解深度学习在量子领域的应用方法。
- 帮助识别模型过拟合和物理可解释性差等风险。
- 适合想用AI推进量子物理、化学研究的研究生与学者。
科学进步与新研究工具的出现紧密相关。如今，机器学习——尤其是深度学习——已成为量子科学与技术中变革性的工具。由于量子系统的内在复杂性，深度学习能够高效探索大规模参数空间、从实验数据中提取模式，并以数据驱动方式指导研究方向。这些能力已应用于优化量子控制协议、加速具有特定量子性质材料的发现，使机器学习/深度学习素养成为下一代量子科学家的必备技能。同时，深度学习也带来风险：模型可能对噪声数据过拟合、掩盖因果结构，并产生物理可解释性有限的结果。正确认识这些局限并采取缓解策略，对科学严谨性至关重要。本讲义提供面向研究生的全面深度学习入门，融合概念阐述与实践案例，按渐进序列组织，旨在帮助读者判断何时及如何有效应用深度学习，理解其实际约束，并负责任地将AI方法适配于量子物理、化学与工程问题。
原文摘要 · Abstract (English)
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic complexity of quantum systems, DL enables efficient exploration of large parameter spaces, extraction of patterns from experimental data, and data-driven guidance for research directions. These capabilities already support tasks such as refining quantum control protocols and accelerating the discovery of materials with targeted quantum properties, making ML/DL literacy an essential skill for the next generation of quantum scientists. At the same time, DL's power brings risks: models can overfit noisy data, obscure causal structure, and yield results with limited physical interpretability. Recognizing these limitations and deploying mitigation strategies is crucial for scientific rigor. These lecture notes provide a comprehensive, graduate-level introduction to DL for quantum applications, combining conceptual exposition with hands-on examples. Organized as a progressive sequence, they aim to equip readers to decide when and how to apply DL effectively, to understand its practical constraints, and to adapt AI methods responsibly to problems across quantum physics, chemistry, and engineering.
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