Machine learning quantum phases of matter beyond the fermion sign problem

Abstract State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion sy...

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Autores principales:	Peter Broecker, Juan Carrasquilla, Roger G. Melko, Simon Trebst
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2436c67e977c43d480a0a608687695d2
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spelling	oai:doaj.org-article:2436c67e977c43d480a0a608687695d22021-12-02T12:32:19ZMachine learning quantum phases of matter beyond the fermion sign problem10.1038/s41598-017-09098-02045-2322https://doaj.org/article/2436c67e977c43d480a0a608687695d22017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-09098-0https://doaj.org/toc/2045-2322Abstract State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion systems such that they correctly identify and locate quantum phase transitions in such systems. Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green’s function holds sufficient information to allow for the distinction of different fermionic phases via a CNN. We demonstrate that this QMC + machine learning approach works even for systems exhibiting a severe fermion sign problem where conventional approaches to extract information from the Green’s function, e.g. in the form of equal-time correlation functions, fail.Peter BroeckerJuan CarrasquillaRoger G. MelkoSimon TrebstNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peter Broecker Juan Carrasquilla Roger G. Melko Simon Trebst Machine learning quantum phases of matter beyond the fermion sign problem
description	Abstract State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion systems such that they correctly identify and locate quantum phase transitions in such systems. Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green’s function holds sufficient information to allow for the distinction of different fermionic phases via a CNN. We demonstrate that this QMC + machine learning approach works even for systems exhibiting a severe fermion sign problem where conventional approaches to extract information from the Green’s function, e.g. in the form of equal-time correlation functions, fail.
format	article
author	Peter Broecker Juan Carrasquilla Roger G. Melko Simon Trebst
author_facet	Peter Broecker Juan Carrasquilla Roger G. Melko Simon Trebst
author_sort	Peter Broecker
title	Machine learning quantum phases of matter beyond the fermion sign problem
title_short	Machine learning quantum phases of matter beyond the fermion sign problem
title_full	Machine learning quantum phases of matter beyond the fermion sign problem
title_fullStr	Machine learning quantum phases of matter beyond the fermion sign problem
title_full_unstemmed	Machine learning quantum phases of matter beyond the fermion sign problem
title_sort	machine learning quantum phases of matter beyond the fermion sign problem
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2436c67e977c43d480a0a608687695d2
work_keys_str_mv	AT peterbroecker machinelearningquantumphasesofmatterbeyondthefermionsignproblem AT juancarrasquilla machinelearningquantumphasesofmatterbeyondthefermionsignproblem AT rogergmelko machinelearningquantumphasesofmatterbeyondthefermionsignproblem AT simontrebst machinelearningquantumphasesofmatterbeyondthefermionsignproblem
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Machine learning quantum phases of matter beyond the fermion sign problem

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