Path optimization method for the sign problem caused by fermion determinant
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915281764352000 |
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| author | Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato |
| author_facet | Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato |
| contents | The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02804 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Path optimization method for the sign problem caused by fermion determinant Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato High Energy Physics - Lattice High Energy Physics - Phenomenology The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. |
| title | Path optimization method for the sign problem caused by fermion determinant |
| topic | High Energy Physics - Lattice High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2502.02804 |