This book provides an accessible introduction to machine learning and demonstrates its applications in the quantum sciences. Readers will be equipped with the necessary tools to engage with emerging literature on machine learning in science and will develop an understanding of its broader impact on science and technology.
This book provides an accessible introduction to machine learning and demonstrates its applications in the quantum sciences. Readers will be equipped with the necessary tools to engage with emerging literature on machine learning in science and will develop an understanding of its broader impact on science and technology.
Anna Dawid is a research fellow at the Flatiron Institute, New York, with the Ph.D. in quantum physics awarded by the University of Warsaw and ICFO, Barcelona. Her research spans interpretable machine learning for scientific discovery, quantum simulations, and foundations of deep learning.
Inhaltsangabe
Preface Acknowledgments List of acronyms Nomenclature 1. Introduction 2. Basics of machine learning 3. Phase classification 4. Gaussian processes and other kernel methods 5. Neural-network quantum states 6. Reinforcement learning 7. Deep learning for quantum sciences-selected topics 8. Physics for deep learning 9. Conclusion and outlook A. Mathematical details on principal component analysis B. Derivation of the kernel trick C. Choosing the kernel matrix as the covariance matrix for a Gaussian process References Index.
Preface Acknowledgments List of acronyms Nomenclature 1. Introduction 2. Basics of machine learning 3. Phase classification 4. Gaussian processes and other kernel methods 5. Neural-network quantum states 6. Reinforcement learning 7. Deep learning for quantum sciences-selected topics 8. Physics for deep learning 9. Conclusion and outlook A. Mathematical details on principal component analysis B. Derivation of the kernel trick C. Choosing the kernel matrix as the covariance matrix for a Gaussian process References Index.
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