  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.
