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

• URL: http://arxiv.org/abs/2406.09077v1
• Date: Thu, 13 Jun 2024 13:01:50 GMT
• Title: Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
• Authors: Imelda Romero, Jannes Nys, Giuseppe Carleo,
• Abstract summary: We introduce a framework for embedding lattice symmetries in Neural Slater-Backflow-Jastrow wavefunction ansatzes. We demonstrate how our model allows us to target the ground state and low-lying excited states.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have shown to be a powerful tool to represent ground state of quantum many-body systems, including for fermionic systems. In this work, we introduce a framework for embedding lattice symmetries in Neural Slater-Backflow-Jastrow wavefunction ansatzes, and demonstrate how our model allows us to target the ground state and low-lying excited states. To capture the Hamiltonian symmetries, we introduce group-equivariant backflow transformations. We study the low-energy excitation spectrum of the t-V model on a square lattice away from half-filling, and find that our symmetry-aware backflow significantly improves the ground-state energies, and yields accurate low-lying excited states for up to 10x10 lattices. We additionally compute the two-point density-correlation function and the structure factor to detect the phase transition and determine the critical point. Finally, we quantify the variational accuracy of our model using the V-score.

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