A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917832170668032 |
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| author | Fioroni, Lorenzo Gravina, Luca Stefaniak, Justyna Baumgärtner, Alexander Finger, Fabian Dreon, Davide Donner, Tobias |
| author_facet | Fioroni, Lorenzo Gravina, Luca Stefaniak, Justyna Baumgärtner, Alexander Finger, Fabian Dreon, Davide Donner, Tobias |
| contents | TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14401 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED Fioroni, Lorenzo Gravina, Luca Stefaniak, Justyna Baumgärtner, Alexander Finger, Fabian Dreon, Davide Donner, Tobias Computational Physics Quantum Gases Atomic Physics Quantum Physics TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field. |
| title | A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED |
| topic | Computational Physics Quantum Gases Atomic Physics Quantum Physics |
| url | https://arxiv.org/abs/2404.14401 |