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

Understand

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.