Improving neural network performance for solving quantum sign structure
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908573599006720 |
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| author | Ou, Xiaowei Huang, Tianshu Ozolins, Vidvuds |
| author_facet | Ou, Xiaowei Huang, Tianshu Ozolins, Vidvuds |
| contents | Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-trained phase network. We introduce a modified stochastic reconfiguration method that effectively uses differing imaginary time steps to evolve the amplitude and phase. Using a larger time step for phase optimization, this method enables a simultaneous and efficient training of phase and amplitude neural networks. The efficacy of our method is demonstrated on the Heisenberg J_1-J_2 model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving neural network performance for solving quantum sign structure Ou, Xiaowei Huang, Tianshu Ozolins, Vidvuds Quantum Physics Strongly Correlated Electrons Computational Physics Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-trained phase network. We introduce a modified stochastic reconfiguration method that effectively uses differing imaginary time steps to evolve the amplitude and phase. Using a larger time step for phase optimization, this method enables a simultaneous and efficient training of phase and amplitude neural networks. The efficacy of our method is demonstrated on the Heisenberg J_1-J_2 model. |
| title | Improving neural network performance for solving quantum sign structure |
| topic | Quantum Physics Strongly Correlated Electrons Computational Physics |
| url | https://arxiv.org/abs/2510.02051 |