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arXiv:2301.08275

quTARANG: A python GPE solver to study turbulence in quantum systems

Jha, Shawan Kumar, Sachin Rawat, Mahendra Kumar Verma, Pankaj Kumar Mishra

Published Jan 19, 2023• 0 citations
arXivHTMLPDF

Abstract

quTARANG is a Python-based general-purpose Gross-Pitaevskii Equation (GPE) solver. It can solve GPE in 1D, 2D and 3D and has the ability to run on both CPU and GPU. It has been developed to study turbulence in quantum systems, specifically in atomic Bose-Einstein condensates, and can be used to study different quantities, such as the varied spectra associated with quantum turbulence.

DOI
10.48550/arxiv.2301.08275

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quTARANG is a Python-based general-purpose Gross-Pitaevskii Equation (GPE) solver. It can solve GPE in 1D, 2D and 3D and has the ability to run on both CPU and GPU. It has been developed to study turbulence in quantum systems, specifically in atomic Bose-Einstein condensates, and can be used to study different quantities, such as the varied spectra associated with quantum turbulence.
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.
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.
consisting of bosonic atoms that have been cooled and condensed into a single, macroscopic ground state (Fetter, 2009;Pethick & Smith, 2008).These systems can be created in an experimental laboratory and allow for the the exploration of physical phenomenon such as superfluid turbulence (Navon, Gaunt, Smith, & Hadzibabic, 2016;Roche & Barenghi, 2008;White, Anderson, & Bagnato, 2014), chaotic dynamics (Gardiner, 2002;Kyriakopoulos, Koukouloyannis, Skokos, & Kevrekidis, 2014;Zhang, 2017), and analogues of other quantum systems (Dalibard, Gerbier, Juzeliūnas, & Öhberg, 2011).Numerical simulations of BECs that directly mimic experiments are valuable to fundamental research in these areas and allow for theoretical advances before experimental validation.The dynamics of BEC systems can be found by solving the non-linear Schrödinger equation known as the Gross-Pitaevskii Equation (GPE),
Many theoretical studies and experimental results rely on the use of numerical analysis for the solution of the Schrodinger equation. Indeed, for nontrivial quantum systems, a complete solution of the dynamics is difficult to achieve analytically. We extended the implementation of a highly optimized solver to simulate the evolution of a wave function on a 2D lattice. We also implemented the imaginary time evolution to approximate the ground state. The dynamics of the system is now described by a Hamiltonian that includes an external potential and a contact interaction term. The algorithm is based on the second-order Trotter-Suzuki approximation and it is implemented on CPU and GPU kernels that run efficiently on a cluster. We proved the accuracy of the code solving the Gross-Pitaevskii equation for a Bose-Einstein condensate and reproducing the experimental results, obtained at NIST, of the soliton dynamics in a cloud of sodium atoms. The code is available under an open source license, and it is exposed as an application program interface and a command-line interface. The code is also accessible in Python and MATLAB. Future development of the code include the extension to a 3D lattice, whereas the actual implementation can already find applications in ultracold atom physics.
The aim of this paper is to present the extension of the HPC pseudo-spectral solver BEC2HPC to compute the dynamics of 2D/3D rotating Bose-Einstein condensates modeled by the Gross-Pitaevskii equations with a rotation term. Numerical examples are provided to show the efficiency of the solver for large-scale simulations. NEW VERSION PROGRAM SUMMARY Program Title: BEC2HPC CPC Library link to program files: https://doi.org/10.17632/mdzpw4dr4t.2 Licensing provisions: GPLv2 Programming language: C++, Python Journal reference of previous version: Comput. Phys. Commun. 265 (2021) 108007, https://doi.org/10.1016/j.cpc.2021.108007 Does the new version supersede the previous version?: Yes Reasons for the new version: The first version of BEC2HPC provided a stationary-state solver, while this new version also enables the computation of the dynamics of rotating Gross–Pitaevskii equations. Summary of revisions: We have implemented ADI time-splitting and relaxation pseudo-spectral schemes in BEC2HPC, following the same parallel design and domain decomposition strategy as for the stationary-state solver. Nature of problem: This extension of the software BEC2HPC computes the dynamics of rotating Bose–Einstein condensates (BEC) modeled by the Gross–Pitaevskii equation (GPE). The available numerical methods are accurate and efficient for BEC with large rotation speeds and high nonlinearities. The parallel implementation allows performing large-scale simulations of 2D or 3D problems on parallel computing platforms. Solution method: Four time discretization methods are implemented for the dynamics of the GPE: a first-, second- and a fourth-order splitting schemes, and a second-order relaxation scheme. The spatial discretization is based on a Fourier pseudo-spectral method. This solver is implemented in distributed memory using MPI and a decomposition of the computational domain. Additional comments including restrictions and unusual features: The parallel algorithms are implemented in C++ and MPI, with a Python interface available for defining the physics of the problem. A pure Python version of sequential algorithms is also provided for rapid prototyping using the same interface. Results can be exported and visualized with external tools (HDF5 and VTK formats for ParaView or PyVista).