How To Use Neural Networks To Investigate Quantum Many-Body Physics

Over the past few years, machine learning has emerged as a powerful computational tool to tackle complex problems in a broad range of scientific disciplines. In particular, artificial neural networks have been successfully used to mitigate the exponential complexity often encountered in quantum many-body physics, the study of properties of quantum systems built from a large number of interacting particles. In this article, we review some applications of neural networks in condensed matter physics and quantum information, with particular emphasis on hands-on tutorials serving as a quick start for a newcomer to the field. The prerequisites of this tutorial are basic probability theory and calculus, linear algebra, basic notions of neural networks, statistical physics, and quantum mechanics. The reader is introduced to supervised machine learning with convolutional neural networks to learn a phase transition, unsupervised learning with restricted Boltzmann machines to perform quantum tomography, and the variational Monte Carlo method with recurrent neural networks for approximating the ground state of a many-body Hamiltonian. For each algorithm, we briefly review the key ingredients and their corresponding neural network implementation, and show numerical experiments for a system of interacting Rydberg atoms in two dimensions.

Automated summary

This article reviews the applications of machine learning in condensed matter physics and quantum information, with a focus on providing hands-on tutorials for newcomers. The prerequisites for readers include basic knowledge of probability theory, calculus, linear algebra, neural networks, statistical physics, and quantum mechanics. The algorithms covered include supervised learning with convolutional neural networks, unsupervised learning with restricted Boltzmann machines, and the variational Monte Carlo method with recurrent neural networks.