Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913617747640320 |
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| author | Nomura, Yusuke Imada, Masatoshi |
| author_facet | Nomura, Yusuke Imada, Masatoshi |
| contents | With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_02633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems Nomura, Yusuke Imada, Masatoshi Strongly Correlated Electrons Disordered Systems and Neural Networks Superconductivity Computational Physics Quantum Physics With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation. |
| title | Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems |
| topic | Strongly Correlated Electrons Disordered Systems and Neural Networks Superconductivity Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2410.02633 |