Fermionic neural Gibbs states
This provides a scalable approach for studying finite-temperature properties of strongly correlated fermionic systems beyond one dimension, addressing a bottleneck in quantum physics simulations.
The researchers tackled the problem of modeling finite-temperature properties of strongly interacting fermions by introducing fermionic neural Gibbs states (fNGS), which accurately reproduced thermal energies for the doped Fermi-Hubbard model across various temperatures, interaction strengths, and large dopings, for system sizes beyond exact methods.
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.