Tackling the Sign Problem in the Doped Hubbard Model with Normalizing Flows
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914408546959360 |
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| author | Schuh, Dominic Funcke, Lena Kreit, Janik Luu, Thomas Singh, Simran |
| author_facet | Schuh, Dominic Funcke, Lena Kreit, Janik Luu, Thomas Singh, Simran |
| contents | The Hubbard model at finite chemical potential is a cornerstone for understanding doped correlated systems, but simulations are severely limited by the sign problem. In the auxiliary-field formulation, the spin basis mitigates the sign problem, yet severe ergodicity issues have limited its use. We extend recent advances with normalizing flows at half-filling to finite chemical potential by introducing an annealing scheme enabling ergodic sampling. Compared to state-of-the-art hybrid Monte Carlo in the charge basis, our approach accurately reproduces exact diagonalization results while reducing statistical uncertainties by an order of magnitude, opening a new path for simulations of doped correlated systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18205 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tackling the Sign Problem in the Doped Hubbard Model with Normalizing Flows Schuh, Dominic Funcke, Lena Kreit, Janik Luu, Thomas Singh, Simran Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice The Hubbard model at finite chemical potential is a cornerstone for understanding doped correlated systems, but simulations are severely limited by the sign problem. In the auxiliary-field formulation, the spin basis mitigates the sign problem, yet severe ergodicity issues have limited its use. We extend recent advances with normalizing flows at half-filling to finite chemical potential by introducing an annealing scheme enabling ergodic sampling. Compared to state-of-the-art hybrid Monte Carlo in the charge basis, our approach accurately reproduces exact diagonalization results while reducing statistical uncertainties by an order of magnitude, opening a new path for simulations of doped correlated systems. |
| title | Tackling the Sign Problem in the Doped Hubbard Model with Normalizing Flows |
| topic | Strongly Correlated Electrons Machine Learning High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2603.18205 |