Modern applications of machine learning in quantum sciences

TL;DR

This paper provides a comprehensive overview of recent machine learning techniques applied to quantum sciences, covering various algorithms and specialized topics to advance quantum research and applications.

Contribution

It offers a detailed synthesis of recent developments in machine learning methods tailored for quantum sciences, including new algorithms and specialized approaches.

Findings

Deep learning and kernel methods enhance quantum state representation.

Machine learning improves quantum phase classification and circuit optimization.

Differentiable programming and generative models are increasingly used in quantum applications.

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.