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dc.contributor.author
Carleo, Giuseppe
dc.contributor.author
Nomura, Yusuke
dc.contributor.author
Imada, Masatoshi
dc.date.accessioned
2019-01-04T15:20:33Z
dc.date.available
2018-12-26T03:28:53Z
dc.date.available
2019-01-04T15:20:33Z
dc.date.issued
2018-12-14
dc.identifier.issn
2041-1723
dc.identifier.other
10.1038/s41467-018-07520-3
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/313237
dc.identifier.doi
10.3929/ethz-b-000313237
dc.description.abstract
Obtaining accurate properties of many-body interacting quantum matter is a long-standing challenge in theoretical physics and chemistry, rooting into the complexity of the many-body wave-function. Classical representations of many-body states constitute a key tool for both analytical and numerical approaches to interacting quantum problems. Here, we introduce a technique to construct classical representations of many-body quantum systems based on artificial neural networks. Our constructions are based on the deep Boltzmann machine architecture, in which two layers of hidden neurons mediate quantum correlations. The approach reproduces the exact imaginary-time evolution for many-body lattice Hamiltonians, is completely deterministic, and yields networks with a polynomially-scaling number of neurons. We provide examples where physical properties of spin Hamiltonians can be efficiently obtained. Also, we show how systematic improvements upon existing restricted Boltzmann machines ansatze can be obtained. Our method is an alternative to the standard path integral and opens new routes in representing quantum many-body states.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Constructing exact representations of quantum many-body systems with deep neural networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Nature Communications
ethz.journal.volume
9
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Nat Commun
ethz.pages.start
5322
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2018-12-26T03:28:54Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-04T15:20:41Z
ethz.rosetta.lastUpdated
2020-02-15T16:29:51Z
ethz.rosetta.versionExported
true
ethz.COinS
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