Work

Boltzmann machines and quantum many-body problems

Year: 2023

Type: article

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.

Source: Journal of Physics Condensed Matter

DOI: 10.1088/1361-648x/ad0916

Author: Yusuke Nomura

Institution: Keio University

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,075

Funders: Japan Science and Technology Corporation, Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science