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arxiv: 2404.14401 · v3 · pith:XE67GRPJnew · submitted 2024-04-22 · ⚛️ physics.comp-ph · cond-mat.quant-gas· physics.atom-ph· quant-ph

A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED

classification ⚛️ physics.comp-ph cond-mat.quant-gasphysics.atom-phquant-ph
keywords torchgpecavityequationgross-pitaevskiimany-bodypackagepotentialspython
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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.

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  1. Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation

    quant-ph 2025-07 unverdicted novelty 5.0

    A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.