Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910806431498240 |
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| author | Romero, Imelda Nys, Jannes Carleo, Giuseppe |
| author_facet | Romero, Imelda Nys, Jannes Carleo, Giuseppe |
| contents | 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 x 10. We also compute accurate two-point density-correlation functions and the structure factor to identify phase transitions and critical points. These findings introduce a symmetry-aware framework important for studying quantum materials and phase transitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations Romero, Imelda Nys, Jannes Carleo, Giuseppe Strongly Correlated Electrons Other Condensed Matter Computational Physics Quantum Physics 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 x 10. We also compute accurate two-point density-correlation functions and the structure factor to identify phase transitions and critical points. These findings introduce a symmetry-aware framework important for studying quantum materials and phase transitions. |
| title | Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations |
| topic | Strongly Correlated Electrons Other Condensed Matter Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2406.09077 |