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Free keywords:
Quantum Physics, quant-ph, Condensed Matter, Disordered Systems and Neural Networks, cond-mat.dis-nn, Condensed Matter, Mesoscale and Nanoscale Physics, cond-mat.mes-hall
Abstract:
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.
