Generalization properties of neural network approximations to frustrated magnet ground states
Generalization properties of neural network approximations to frustrated magnet ground states
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Neural quantum states (NQS) attract a lot of attention due to their potential to serve as a very expressive variational ansatz for quantum many-body systems. Here we study the main factors governing the applicability of NQS to frustrated magnets by training neural networks to approximate ground states of several moderately-sized Hamiltonians using...
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Generalization properties of neural network approximations to frustrated magnet ground states
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TN_cdi_doaj_primary_oai_doaj_org_article_7fb32dcccfe64492aaf5f24493ed45b7
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7fb32dcccfe64492aaf5f24493ed45b7
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-020-15402-w
