Contributor info: Dr Fabian Finger

Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak, Alexander Baumgärtner, Fabian Finger, Davide Dreon, Tobias Donner

SciPost Phys. Codebases 38 (2024) · published 6 November 2024 | Toggle abstract · pdf

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.

Lorenzo Fioroni, Luca Gravina, Justyna Stefaniak, Alexander Baumgärtner, Fabian Finger, Davide Dreon, Tobias Donner

SciPost Phys. Codebases 38-r1.0 (2024) · published 6 November 2024 | Toggle abstract · src

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.