Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916802950332416 |
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| author | Schuh, Dominic Kreit, Janik Berkowitz, Evan Funcke, Lena Luu, Thomas Nicoli, Kim A. Rodekamp, Marcel |
| author_facet | Schuh, Dominic Kreit, Janik Berkowitz, Evan Funcke, Lena Luu, Thomas Nicoli, Kim A. Rodekamp, Marcel |
| contents | We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the electronic structure of graphene and related materials. State-of-the-art methods like Hybrid Monte Carlo often suffer from ergodicity issues near the time-continuum limit, leading to biased estimates. Leveraging symmetry-aware architectures as well as independent and identically distributed sampling, our approach resolves these issues and achieves significant speed-ups over traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows Schuh, Dominic Kreit, Janik Berkowitz, Evan Funcke, Lena Luu, Thomas Nicoli, Kim A. Rodekamp, Marcel Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the electronic structure of graphene and related materials. State-of-the-art methods like Hybrid Monte Carlo often suffer from ergodicity issues near the time-continuum limit, leading to biased estimates. Leveraging symmetry-aware architectures as well as independent and identically distributed sampling, our approach resolves these issues and achieves significant speed-ups over traditional methods. |
| title | Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows |
| topic | Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2506.17015 |