Fermionic neural Gibbs states
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912749040173056 |
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| author | Nys, Jannes Carrasquilla, Juan |
| author_facet | Nys, Jannes Carrasquilla, Juan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fermionic neural Gibbs states Nys, Jannes Carrasquilla, Juan Quantum Physics Strongly Correlated Electrons Machine Learning Computational Physics 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. |
| title | Fermionic neural Gibbs states |
| topic | Quantum Physics Strongly Correlated Electrons Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2512.04663 |