Quantum machine learning for electronic structure calculations

Quantum machine learning for electronic structure calculations

With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body p...

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Bibliographic Details
Main Authors: Rongxin Xia, Sabre Kais
Format: Article
Language:English
Published: Nature Publishing Group 2018-10-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-06598-z
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spelling doaj-2c2ac00115c04322a31d84ce4aae2fb42021-05-11T10:05:38ZengNature Publishing GroupNature Communications2041-17232018-10-01911610.1038/s41467-018-06598-zQuantum machine learning for electronic structure calculationsRongxin Xia0Sabre Kais1Department of Physics and Astronomy, Purdue UniversityDepartment of Physics and Astronomy, Purdue UniversityWith the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body problems.https://doi.org/10.1038/s41467-018-06598-z
collection DOAJ
language English
format Article
sources DOAJ
author Rongxin Xia
Sabre Kais
spellingShingle Rongxin Xia
Sabre Kais
Quantum machine learning for electronic structure calculations
Nature Communications
author_facet Rongxin Xia
Sabre Kais
author_sort Rongxin Xia
title Quantum machine learning for electronic structure calculations
title_short Quantum machine learning for electronic structure calculations
title_full Quantum machine learning for electronic structure calculations
title_fullStr Quantum machine learning for electronic structure calculations
title_full_unstemmed Quantum machine learning for electronic structure calculations
title_sort quantum machine learning for electronic structure calculations
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2018-10-01
description With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body problems.
url https://doi.org/10.1038/s41467-018-06598-z
work_keys_str_mv AT rongxinxia quantummachinelearningforelectronicstructurecalculations
AT sabrekais quantummachinelearningforelectronicstructurecalculations
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