Power of data in quantum machine learning

Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical o...

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Autores principales:	Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod R. McClean
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/076ad8aa820c4993a8091c6ffbf839db
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id	oai:doaj.org-article:076ad8aa820c4993a8091c6ffbf839db
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spelling	oai:doaj.org-article:076ad8aa820c4993a8091c6ffbf839db2021-12-02T17:02:04ZPower of data in quantum machine learning10.1038/s41467-021-22539-92041-1723https://doaj.org/article/076ad8aa820c4993a8091c6ffbf839db2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22539-9https://doaj.org/toc/2041-1723Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.Hsin-Yuan HuangMichael BroughtonMasoud MohseniRyan BabbushSergio BoixoHartmut NevenJarrod R. McCleanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Hsin-Yuan Huang Michael Broughton Masoud Mohseni Ryan Babbush Sergio Boixo Hartmut Neven Jarrod R. McClean Power of data in quantum machine learning
description	Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.
format	article
author	Hsin-Yuan Huang Michael Broughton Masoud Mohseni Ryan Babbush Sergio Boixo Hartmut Neven Jarrod R. McClean
author_facet	Hsin-Yuan Huang Michael Broughton Masoud Mohseni Ryan Babbush Sergio Boixo Hartmut Neven Jarrod R. McClean
author_sort	Hsin-Yuan Huang
title	Power of data in quantum machine learning
title_short	Power of data in quantum machine learning
title_full	Power of data in quantum machine learning
title_fullStr	Power of data in quantum machine learning
title_full_unstemmed	Power of data in quantum machine learning
title_sort	power of data in quantum machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/076ad8aa820c4993a8091c6ffbf839db
work_keys_str_mv	AT hsinyuanhuang powerofdatainquantummachinelearning AT michaelbroughton powerofdatainquantummachinelearning AT masoudmohseni powerofdatainquantummachinelearning AT ryanbabbush powerofdatainquantummachinelearning AT sergioboixo powerofdatainquantummachinelearning AT hartmutneven powerofdatainquantummachinelearning AT jarrodrmcclean powerofdatainquantummachinelearning
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Power of data in quantum machine learning

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