Boltzmann machines and quantum many-body problems
- URL: http://arxiv.org/abs/2306.16877v3
- Date: Wed, 1 Nov 2023 14:54:02 GMT
- Title: Boltzmann machines and quantum many-body problems
- Authors: Yusuke Nomura
- Abstract summary: 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.
This review focuses on Boltzmann machines and provides an overview of recent developments and applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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