Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations.

ABSTRACT: Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In this work, we introduce a framework for embedding lattice symmetries in fermionic wavefunctions and demonstrate its ability to target both ground states and low-lying excitations. Using group-equivariant neural backflow transformations, we study the t-V model on a square lattice away from half-filling. Our symmetry-aware backflow significantly improves ground-state energies and yields accurate low-energy excitations for lattices up to 10 × 10. We also compute accurate two-point density-correlation functions and the structure f

SUBMITTER: Romero I 

PROVIDER: S-EPMC11779646 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature