Approximating Ground States by Neural Network Quantum States

Motivated by the Carleo’s work (Science, 2017, 355: 602), we focus on finding the neural network quantum statesapproximation of the unknown ground state of a given Hamiltonian H in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on th...

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Detalles Bibliográficos
Autores principales: Yang, Ying, Zhang, Chengyang, Cao, Huaixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514192/
https://www.ncbi.nlm.nih.gov/pubmed/33266798
http://dx.doi.org/10.3390/e21010082
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author Yang, Ying
Zhang, Chengyang
Cao, Huaixin
author_facet Yang, Ying
Zhang, Chengyang
Cao, Huaixin
author_sort Yang, Ying
collection PubMed
description Motivated by the Carleo’s work (Science, 2017, 355: 602), we focus on finding the neural network quantum statesapproximation of the unknown ground state of a given Hamiltonian H in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples.
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spelling pubmed-75141922020-11-09 Approximating Ground States by Neural Network Quantum States Yang, Ying Zhang, Chengyang Cao, Huaixin Entropy (Basel) Article Motivated by the Carleo’s work (Science, 2017, 355: 602), we focus on finding the neural network quantum statesapproximation of the unknown ground state of a given Hamiltonian H in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples. MDPI 2019-01-17 /pmc/articles/PMC7514192/ /pubmed/33266798 http://dx.doi.org/10.3390/e21010082 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Ying
Zhang, Chengyang
Cao, Huaixin
Approximating Ground States by Neural Network Quantum States
title Approximating Ground States by Neural Network Quantum States
title_full Approximating Ground States by Neural Network Quantum States
title_fullStr Approximating Ground States by Neural Network Quantum States
title_full_unstemmed Approximating Ground States by Neural Network Quantum States
title_short Approximating Ground States by Neural Network Quantum States
title_sort approximating ground states by neural network quantum states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514192/
https://www.ncbi.nlm.nih.gov/pubmed/33266798
http://dx.doi.org/10.3390/e21010082
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AT zhangchengyang approximatinggroundstatesbyneuralnetworkquantumstates
AT caohuaixin approximatinggroundstatesbyneuralnetworkquantumstates

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