Modern applications of machine learning in quantum sciences
- URL: http://arxiv.org/abs/2204.04198v3
- Date: Wed, 15 Nov 2023 21:36:17 GMT
- Title: Modern applications of machine learning in quantum sciences
- Authors: Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin
P{\l}odzie\'n, Kaelan Donatella, Kim A. Nicoli, Paolo Stornati, Rouven Koch,
Miriam B\"uttner, Robert Oku{\l}a, Gorka Mu\~noz-Gil, Rodrigo A.
Vargas-Hern\'andez, Alba Cervera-Lierta, Juan Carrasquilla, Vedran Dunjko,
Marylou Gabri\'e, Patrick Huembeli, Evert van Nieuwenburg, Filippo Vicentini,
Lei Wang, Sebastian J. Wetzel, Giuseppe Carleo, Eli\v{s}ka Greplov\'a, Roman
Krems, Florian Marquardt, Micha{\l} Tomza, Maciej Lewenstein, Alexandre
Dauphin
- Abstract summary: We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms.
We discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
- Score: 51.09906911582811
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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