Modern applications of machine learning in quantum sciences
Anna Dawid
Julian Arnold
Borja Requena
Alexander Gresch
Marcin Płodzień
Kaelan Donatella
Kim A. Nicoli
Paolo Stornati
Rouven Koch
Miriam Büttner
Robert Okuła
Gorka Muñoz-Gil
Rodrigo A. Vargas-Hernández
Alba Cervera-Lierta
Juan Carrasquilla
Vedran Dunjko
Marylou Gabrié
Patrick Huembeli
Evert van Nieuwenburg
Filippo Vicentini
Lei Wang
Sebastian J. Wetzel
Giuseppe Carleo
Eliška Greplová
Roman Krems
Florian Marquardt
Michał Tomza
Maciej Lewenstein
Alexandre Dauphin
In these Lecture Notes, 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.
