Thermodynamics based on Neural Networks

Fuente: arXiv
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Auteurs principaux: Wagner, D., Klümper, A., Sirker, J.
Format: Preprint
Publié: 2023
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author Wagner, D.
Klümper, A.
Sirker, J.
author_facet Wagner, D.
Klümper, A.
Sirker, J.
contents We present three different neural network algorithms to calculate thermodynamic properties as well as dynamic correlation functions at finite temperatures for quantum lattice models. The first method is based on purification, which allows for the exact calculation of the operator trace. The second one is based on a sampling of the trace using minimally entangled states, whereas the third one makes use of quantum typicality. In the latter case, we approximate a typical infinite-temperature state by wave functions which are given by a product of a projected pair and a neural network part and evolve this typical state in imaginary time.
format Preprint
id arxiv_https___arxiv_org_abs_2311_13799
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Thermodynamics based on Neural Networks
Wagner, D.
Klümper, A.
Sirker, J.
Statistical Mechanics
Disordered Systems and Neural Networks
Strongly Correlated Electrons
We present three different neural network algorithms to calculate thermodynamic properties as well as dynamic correlation functions at finite temperatures for quantum lattice models. The first method is based on purification, which allows for the exact calculation of the operator trace. The second one is based on a sampling of the trace using minimally entangled states, whereas the third one makes use of quantum typicality. In the latter case, we approximate a typical infinite-temperature state by wave functions which are given by a product of a projected pair and a neural network part and evolve this typical state in imaginary time.
title Thermodynamics based on Neural Networks
topic Statistical Mechanics
Disordered Systems and Neural Networks
Strongly Correlated Electrons
url https://arxiv.org/abs/2311.13799