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Modern applications of machine learning in quantum sciences

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Marquardt,  Florian
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;

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2204.04198.pdf
(Preprint), 18MB

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(Supplementary material), 29KB

Citation

Dawid, A., Arnold, J., Requena, B., Gresch, A., Płodzień, M., Donatella, K., et al. (2022). Modern applications of machine learning in quantum sciences. arXiv 2204.04198.


Cite as: https://hdl.handle.net/21.11116/0000-000A-538D-A
Abstract
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.