Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Physical principles underlying machine learning analysis of quantum gas microscopy data are not well understood. Here the authors develop a neural network based approach to classify image data in terms of multi-site correlation functions and reveal the role of fourth-order correlations in the Fermi-...

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Autores principales:	Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/c5f988f964a14deb9630bb1251319555
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spelling	oai:doaj.org-article:c5f988f964a14deb9630bb12513195552021-12-02T17:12:24ZCorrelator convolutional neural networks as an interpretable architecture for image-like quantum matter data10.1038/s41467-021-23952-w2041-1723https://doaj.org/article/c5f988f964a14deb9630bb12513195552021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23952-whttps://doaj.org/toc/2041-1723Physical principles underlying machine learning analysis of quantum gas microscopy data are not well understood. Here the authors develop a neural network based approach to classify image data in terms of multi-site correlation functions and reveal the role of fourth-order correlations in the Fermi-Hubbard model.Cole MilesAnnabelle BohrdtRuihan WuChristie ChiuMuqing XuGeoffrey JiMarkus GreinerKilian Q. WeinbergerEugene DemlerEun-Ah KimNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Cole Miles Annabelle Bohrdt Ruihan Wu Christie Chiu Muqing Xu Geoffrey Ji Markus Greiner Kilian Q. Weinberger Eugene Demler Eun-Ah Kim Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
description	Physical principles underlying machine learning analysis of quantum gas microscopy data are not well understood. Here the authors develop a neural network based approach to classify image data in terms of multi-site correlation functions and reveal the role of fourth-order correlations in the Fermi-Hubbard model.
format	article
author	Cole Miles Annabelle Bohrdt Ruihan Wu Christie Chiu Muqing Xu Geoffrey Ji Markus Greiner Kilian Q. Weinberger Eugene Demler Eun-Ah Kim
author_facet	Cole Miles Annabelle Bohrdt Ruihan Wu Christie Chiu Muqing Xu Geoffrey Ji Markus Greiner Kilian Q. Weinberger Eugene Demler Eun-Ah Kim
author_sort	Cole Miles
title	Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
title_short	Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
title_full	Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
title_fullStr	Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
title_full_unstemmed	Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
title_sort	correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c5f988f964a14deb9630bb1251319555
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Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

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