Mutual information of spin systems from autoregressive neural networks

2023
journal article
article
cris.lastimport.wos2024-04-09T22:52:53Z
dc.abstract.enWe describe a new direct method to estimate bipartite mutual information of a classical spin system based on Monte Carlo sampling enhanced by autoregressive neural networks. It allows studying arbitrary geometries of subsystems and can be generalized to classical field theories. We demonstrate it on the Ising model for four partitionings, including a multiply-connected even-odd division. We show that the area law is satisfied for temperatures away from the critical temperature: the constant term is universal, whereas the proportionality coefficient is different for the even-odd partitioning.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki Teoretycznejpl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanejpl
dc.contributor.authorBiałas, Piotr - 127296 pl
dc.contributor.authorKorcyl, Piotr - 125645 pl
dc.contributor.authorStebel, Tomasz - 150140 pl
dc.date.accessioned2023-10-27T17:55:13Z
dc.date.available2023-10-27T17:55:13Z
dc.date.issued2023pl
dc.description.number4pl
dc.description.volume108pl
dc.identifier.articleid044140pl
dc.identifier.doi10.1103/PhysRevE.108.044140pl
dc.identifier.eissn2470-0053pl
dc.identifier.issn2470-0045pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/322409
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa*
dc.rights.licencebez licencji
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode.pl*
dc.subtypeArticlepl
dc.titleMutual information of spin systems from autoregressive neural networkspl
dc.title.journalPhysical Review. Epl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-09T22:52:53Z
dc.abstract.enpl
We describe a new direct method to estimate bipartite mutual information of a classical spin system based on Monte Carlo sampling enhanced by autoregressive neural networks. It allows studying arbitrary geometries of subsystems and can be generalized to classical field theories. We demonstrate it on the Ising model for four partitionings, including a multiply-connected even-odd division. We show that the area law is satisfied for temperatures away from the critical temperature: the constant term is universal, whereas the proportionality coefficient is different for the even-odd partitioning.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki Teoretycznej
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej
dc.contributor.authorpl
Białas, Piotr - 127296
dc.contributor.authorpl
Korcyl, Piotr - 125645
dc.contributor.authorpl
Stebel, Tomasz - 150140
dc.date.accessioned
2023-10-27T17:55:13Z
dc.date.available
2023-10-27T17:55:13Z
dc.date.issuedpl
2023
dc.description.numberpl
4
dc.description.volumepl
108
dc.identifier.articleidpl
044140
dc.identifier.doipl
10.1103/PhysRevE.108.044140
dc.identifier.eissnpl
2470-0053
dc.identifier.issnpl
2470-0045
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/322409
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa
dc.rights.licence
bez licencji
dc.rights.uri*
http://creativecommons.org/licenses/by/4.0/legalcode.pl
dc.subtypepl
Article
dc.titlepl
Mutual information of spin systems from autoregressive neural networks
dc.title.journalpl
Physical Review. E
dc.typepl
JournalArticle
dspace.entity.type
Publication

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