Deep Learning in Classical and Quantum Physics

2025-08-14

AI Summary

To address the high-dimensional complexity of quantum systems and inherent limitations of deep learning (DL)—including poor physical interpretability, weak causal modeling capability, and susceptibility to overfitting—this work proposes a systematic DL application framework tailored for quantum science. Methodologically, it integrates supervised learning, pattern recognition, and data-driven modeling while embedding quantum-mechanical priors to constrain model architecture and enhance causal interpretability. The framework is applied to quantum control optimization and quantum materials discovery, enabling efficient large-parameter-space search and experimental-data-driven scientific hypothesis generation. Key contributions include: (1) the first quantum-DL co-design methodology ensuring both physical consistency and AI robustness; (2) a graduate-level interdisciplinary curriculum that rigorously defines DL’s applicability boundaries and validation protocols; and (3) practical guidelines for responsible AI deployment in quantum technologies, advancing ML from a “black-box tool” to a “trustworthy scientific partner.”

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Exploring large quantum parameter spaces efficiently using deep learning
• Extracting patterns from noisy quantum experimental data accurately
• Balancing DL benefits and risks in quantum physics applications

Innovation

• Deep learning explores large quantum parameter spaces
• DL extracts patterns from quantum experimental data
• AI methods adapt responsibly to quantum physics