Taming Landau level mixing in fractional quantum Hall states with deep learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913815510122496 |
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| author | Qian, Yubing Zhao, Tongzhou Zhang, Jianxiao Xiang, Tao Li, Xiang Chen, Ji |
| author_facet | Qian, Yubing Zhao, Tongzhou Zhang, Jianxiao Xiang, Tao Li, Xiang Chen, Ji |
| contents | Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall effect is a prominent example of strong correlation, with Landau level mixing being one of the most challenging aspects to address using traditional computational methods. Deep learning real-space neural network wavefunction methods have emerged as promising architectures to describe electron correlations in molecules and materials, but their power has not been fully tested for exotic quantum states. In this work, we employ real-space neural network wavefunction techniques to investigate fractional quantum Hall systems. On both $1/3$ and $2/5$ filling systems, we achieve energies consistently lower than exact diagonalization results which only consider the lowest Landau level. We also demonstrate that the real-space neural network wavefunction can naturally capture the extent of Landau level mixing up to a very high level, overcoming the limitations of traditional methods. Our work underscores the potential of neural networks for future studies of strongly correlated systems and opens new avenues for exploring the rich physics of the fractional quantum Hall effect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Taming Landau level mixing in fractional quantum Hall states with deep learning Qian, Yubing Zhao, Tongzhou Zhang, Jianxiao Xiang, Tao Li, Xiang Chen, Ji Strongly Correlated Electrons Mesoscale and Nanoscale Physics Computational Physics Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall effect is a prominent example of strong correlation, with Landau level mixing being one of the most challenging aspects to address using traditional computational methods. Deep learning real-space neural network wavefunction methods have emerged as promising architectures to describe electron correlations in molecules and materials, but their power has not been fully tested for exotic quantum states. In this work, we employ real-space neural network wavefunction techniques to investigate fractional quantum Hall systems. On both $1/3$ and $2/5$ filling systems, we achieve energies consistently lower than exact diagonalization results which only consider the lowest Landau level. We also demonstrate that the real-space neural network wavefunction can naturally capture the extent of Landau level mixing up to a very high level, overcoming the limitations of traditional methods. Our work underscores the potential of neural networks for future studies of strongly correlated systems and opens new avenues for exploring the rich physics of the fractional quantum Hall effect. |
| title | Taming Landau level mixing in fractional quantum Hall states with deep learning |
| topic | Strongly Correlated Electrons Mesoscale and Nanoscale Physics Computational Physics |
| url | https://arxiv.org/abs/2412.14795 |