Generalization properties of neural network approximations to frustrated magnet ground states

Neural network representations of quantum states are hoped to provide an efficient basis for numerical methods without the need for case-by-case trial wave functions. Here the authors show that limited generalization capacity of such representations is responsible for convergence problems for frustr...

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Autores principales:	Tom Westerhout, Nikita Astrakhantsev, Konstantin S. Tikhonov, Mikhail I. Katsnelson, Andrey A. Bagrov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7fb32dcccfe64492aaf5f24493ed45b7
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spelling	oai:doaj.org-article:7fb32dcccfe64492aaf5f24493ed45b72021-12-02T14:40:54ZGeneralization properties of neural network approximations to frustrated magnet ground states10.1038/s41467-020-15402-w2041-1723https://doaj.org/article/7fb32dcccfe64492aaf5f24493ed45b72020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15402-whttps://doaj.org/toc/2041-1723Neural network representations of quantum states are hoped to provide an efficient basis for numerical methods without the need for case-by-case trial wave functions. Here the authors show that limited generalization capacity of such representations is responsible for convergence problems for frustrated systems.Tom WesterhoutNikita AstrakhantsevKonstantin S. TikhonovMikhail I. KatsnelsonAndrey A. BagrovNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tom Westerhout Nikita Astrakhantsev Konstantin S. Tikhonov Mikhail I. Katsnelson Andrey A. Bagrov Generalization properties of neural network approximations to frustrated magnet ground states
description	Neural network representations of quantum states are hoped to provide an efficient basis for numerical methods without the need for case-by-case trial wave functions. Here the authors show that limited generalization capacity of such representations is responsible for convergence problems for frustrated systems.
format	article
author	Tom Westerhout Nikita Astrakhantsev Konstantin S. Tikhonov Mikhail I. Katsnelson Andrey A. Bagrov
author_facet	Tom Westerhout Nikita Astrakhantsev Konstantin S. Tikhonov Mikhail I. Katsnelson Andrey A. Bagrov
author_sort	Tom Westerhout
title	Generalization properties of neural network approximations to frustrated magnet ground states
title_short	Generalization properties of neural network approximations to frustrated magnet ground states
title_full	Generalization properties of neural network approximations to frustrated magnet ground states
title_fullStr	Generalization properties of neural network approximations to frustrated magnet ground states
title_full_unstemmed	Generalization properties of neural network approximations to frustrated magnet ground states
title_sort	generalization properties of neural network approximations to frustrated magnet ground states
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7fb32dcccfe64492aaf5f24493ed45b7
work_keys_str_mv	AT tomwesterhout generalizationpropertiesofneuralnetworkapproximationstofrustratedmagnetgroundstates AT nikitaastrakhantsev generalizationpropertiesofneuralnetworkapproximationstofrustratedmagnetgroundstates AT konstantinstikhonov generalizationpropertiesofneuralnetworkapproximationstofrustratedmagnetgroundstates AT mikhailikatsnelson generalizationpropertiesofneuralnetworkapproximationstofrustratedmagnetgroundstates AT andreyabagrov generalizationpropertiesofneuralnetworkapproximationstofrustratedmagnetgroundstates
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Generalization properties of neural network approximations to frustrated magnet ground states

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