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CITATIONS.bib

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@misc{qian_taming_2024,
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title = {Taming {{Landau}} Level Mixing in Fractional Quantum {{Hall}} States with Deep Learning},
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@article{PhysRevLett.134.176503,
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title = {Describing Landau Level Mixing in Fractional Quantum Hall States with Deep Learning},
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author = {Qian, Yubing and Zhao, Tongzhou and Zhang, Jianxiao and Xiang, Tao and Li, Xiang and Chen, Ji},
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year = {2024},
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month = dec,
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number = {arXiv:2412.14795},
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eprint = {2412.14795},
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primaryclass = {cond-mat},
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publisher = {arXiv},
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doi = {10.48550/arXiv.2412.14795},
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urldate = {2024-12-23},
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archiveprefix = {arXiv}
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journal = {Phys. Rev. Lett.},
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volume = {134},
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issue = {17},
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pages = {176503},
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numpages = {8},
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year = {2025},
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month = {Apr},
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publisher = {American Physical Society},
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doi = {10.1103/PhysRevLett.134.176503},
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url = {https://link.aps.org/doi/10.1103/PhysRevLett.134.176503}
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}

README.md

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<p align="center">
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<img src=".github/img/deephall.png">
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<img src=".github/img/deephall.svg" width="200">
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</p>
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<h1 align="center">DeepHall</h1>
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Simulating the fractional quantum Hall effect (FQHE) with neural network variational Monte Carlo.
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This repository contains the codebase for the paper [Taming Landau level mixing in fractional quantum Hall states with deep learning](https://arxiv.org/abs/2412.14795). If you use this code in your work, please [cite our paper](CITATIONS.bib).
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This repository contains the codebase for the paper [Describing Landau Level Mixing in Fractional Quantum Hall States with Deep Learning](https://doi.org/10.1103/PhysRevLett.134.176503). If you use this code in your work, please [cite our paper](CITATIONS.bib).
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Currently, DeepHall supports running simulations with spin-polarized electrons on a sphere and has been tested with 1/3 and 2/5 fillings.
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If you use this code in your work, please cite the following paper:
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```bib
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@misc{qian_taming_2024,
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title = {Taming {{Landau}} Level Mixing in Fractional Quantum {{Hall}} States with Deep Learning},
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@article{PhysRevLett.134.176503,
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title = {Describing Landau Level Mixing in Fractional Quantum Hall States with Deep Learning},
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author = {Qian, Yubing and Zhao, Tongzhou and Zhang, Jianxiao and Xiang, Tao and Li, Xiang and Chen, Ji},
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year = {2024},
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month = dec,
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number = {arXiv:2412.14795},
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eprint = {2412.14795},
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primaryclass = {cond-mat},
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publisher = {arXiv},
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doi = {10.48550/arXiv.2412.14795},
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urldate = {2024-12-23},
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archiveprefix = {arXiv}
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journal = {Phys. Rev. Lett.},
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volume = {134},
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issue = {17},
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pages = {176503},
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numpages = {8},
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year = {2025},
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month = {Apr},
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publisher = {American Physical Society},
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doi = {10.1103/PhysRevLett.134.176503},
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url = {https://link.aps.org/doi/10.1103/PhysRevLett.134.176503}
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}
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```

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