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

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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Main Authors: Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-23952-w
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spelling doaj-c5f988f964a14deb9630bb12513195552021-06-27T11:12:34ZengNature Publishing GroupNature Communications2041-17232021-06-011211710.1038/s41467-021-23952-wCorrelator convolutional neural networks as an interpretable architecture for image-like quantum matter dataCole Miles0Annabelle Bohrdt1Ruihan Wu2Christie Chiu3Muqing Xu4Geoffrey Ji5Markus Greiner6Kilian Q. Weinberger7Eugene Demler8Eun-Ah Kim9Department of Physics, Cornell UniversityDepartment of Physics, Harvard UniversityDepartment of Computer Science, Cornell UniversityDepartment of Physics, Harvard UniversityDepartment of Physics, Harvard UniversityDepartment of Physics, Harvard UniversityDepartment of Physics, Harvard UniversityDepartment of Computer Science, Cornell UniversityDepartment of Physics, Harvard UniversityDepartment of Physics, Cornell UniversityPhysical 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.https://doi.org/10.1038/s41467-021-23952-w
collection DOAJ
language English
format Article
sources DOAJ
author Cole Miles
Annabelle Bohrdt
Ruihan Wu
Christie Chiu
Muqing Xu
Geoffrey Ji
Markus Greiner
Kilian Q. Weinberger
Eugene Demler
Eun-Ah Kim
spellingShingle 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
Nature Communications
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 Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-06-01
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.
url https://doi.org/10.1038/s41467-021-23952-w
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