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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.02644 |
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| _version_ | 1866916836522590208 |
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| author | Gu, Yuntian Li, Wenrui Lin, Heng Zhan, Bo Li, Ruichen Huang, Yifei He, Di Wu, Yantao Xiang, Tao Qin, Mingpu Wang, Liwei Lv, Dingshun |
| author_facet | Gu, Yuntian Li, Wenrui Lin, Heng Zhan, Bo Li, Ruichen Huang, Yifei He, Di Wu, Yantao Xiang, Tao Qin, Mingpu Wang, Liwei Lv, Dingshun |
| contents | The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edge transformer-based architectures and developing highly efficient optimization algorithms, we achieve the state-of-the-art results for the doped two-dimensional (2D) Hubbard model, arguably the minimum model for high-Tc superconductivity. Interestingly, we find different attention heads in the NQS ansatz can directly encode correlations at different scales, making it capable of capturing long-range correlations and entanglements in strongly correlated systems. With these advances, we establish the half-filled stripe in the ground state of 2D Hubbard model with the next nearest neighboring hoppings, consistent with experimental observations in cuprates. Our work establishes NQS as a powerful tool for solving challenging many-fermions systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving the Hubbard model with Neural Quantum States Gu, Yuntian Li, Wenrui Lin, Heng Zhan, Bo Li, Ruichen Huang, Yifei He, Di Wu, Yantao Xiang, Tao Qin, Mingpu Wang, Liwei Lv, Dingshun Strongly Correlated Electrons Artificial Intelligence Quantum Physics The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edge transformer-based architectures and developing highly efficient optimization algorithms, we achieve the state-of-the-art results for the doped two-dimensional (2D) Hubbard model, arguably the minimum model for high-Tc superconductivity. Interestingly, we find different attention heads in the NQS ansatz can directly encode correlations at different scales, making it capable of capturing long-range correlations and entanglements in strongly correlated systems. With these advances, we establish the half-filled stripe in the ground state of 2D Hubbard model with the next nearest neighboring hoppings, consistent with experimental observations in cuprates. Our work establishes NQS as a powerful tool for solving challenging many-fermions systems. |
| title | Solving the Hubbard model with Neural Quantum States |
| topic | Strongly Correlated Electrons Artificial Intelligence Quantum Physics |
| url | https://arxiv.org/abs/2507.02644 |