Boltzmann machines and quantum many-body problems

TL;DR

This paper reviews how Boltzmann machines, a type of neural network, are used to analyze complex quantum many-body problems by embedding quantum entanglement into their structure.

Contribution

It provides an overview of recent developments and applications of Boltzmann machines in quantum many-body physics, highlighting their emerging role as analytical tools.

Findings

Boltzmann machines effectively model quantum entanglement.

Recent applications demonstrate their utility in quantum state analysis.

The approach offers new insights into quantum correlations.

Abstract

Analyzing quantum many-body problems and elucidating the entangled structure of quantum states is a significant challenge common to a wide range of fields. Recently, a novel approach using machine learning was introduced to address this challenge. The idea is to "embed" nontrivial quantum correlations (quantum entanglement) into artificial neural networks. Through intensive developments, artificial neural network methods are becoming new powerful tools for analyzing quantum many-body problems. Among various artificial neural networks, this topical review focuses on Boltzmann machines and provides an overview of recent developments and applications.