{"ID":2832929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04663","arxiv_id":"2512.04663","title":"Fermionic neural Gibbs states","abstract":"We introduce fermionic neural Gibbs states (fNGS), a variational framework for modeling finite-temperature properties of strongly interacting fermions. fNGS starts from a reference mean-field thermofield-double state and uses neural-network transformations together with imaginary-time evolution to systematically build strong correlations. Applied to the doped Fermi-Hubbard model, a minimal lattice model capturing essential features of strong electronic correlations, fNGS accurately reproduces thermal energies over a broad range of temperatures, interaction strengths, even at large dopings, for system sizes beyond the reach of exact methods. These results demonstrate a scalable route to studying finite-temperature properties of strongly correlated fermionic systems beyond one dimension with neural-network representations of quantum states.","short_abstract":"We introduce fermionic neural Gibbs states (fNGS), a variational framework for modeling finite-temperature properties of strongly interacting fermions. fNGS starts from a reference mean-field thermofield-double state and uses neural-network transformations together with imaginary-time evolution to systematically build...","url_abs":"https://arxiv.org/abs/2512.04663","url_pdf":"https://arxiv.org/pdf/2512.04663v1","authors":"[\"Jannes Nys\",\"Juan Carrasquilla\"]","published":"2025-12-04T10:54:37Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cond-mat.str-el\",\"cs.LG\",\"physics.comp-ph\"]","methods":"[]","has_code":false}