QSW_MPI: A framework for parallel simulation of quantum stochastic walks

Edric Matwiejew; Jingbo Wang

doi:10.1016/j.cpc.2020.107724

QSW_MPI: A framework for parallel simulation of quantum stochastic walks

Edric Matwiejew, Jingbo Wang

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

QSW_MPI is a Python package developed for time-series simulation of continuous-time quantum stochastic walks. This model allows for the study of Markovian open quantum systems in the Lindblad formalism, including a generalisation of the continuous-time random walk and continuous-time quantum walk. Consisting of a Python interface accessing parallelised Fortran libraries utilising sparse data structures, QSW_MPI is scalable to massively parallel computers, which makes possible the simulation of a wide range of walk dynamics on directed and undirected graphs of arbitrary complexity. Program summary: Program Title: QSW_MPI CPC Library link to program files: http://dx.doi.org/10.17632/r8jsx4xxgv.1 Licensing provisions: GPLv3 Programming language: Python 3 + Fortran 2003 External routines/libraries: NumPy [1], SciPy [2], MPI for Python [3], h5py[4] Nature of problem: QSW_MPI provides a framework for the simulation of quantum stochastic walks on arbitrary graphs (directed/undirected, weighted/unweighted). Solution method: A parallel distributed-memory implementation of the matrix exponential via a truncated Taylor series expansion with scaling and squaring [5]. Additional comments including restrictions and unusual features: QSW_MPI will provide support for the simulation of multiple quantum walkers in a future version. References: [1] T. Oliphant, NumPy: A guide to NumPy, 2006, published: USA: Trelgol Publishing. [2] E. Jones, T. Oliphant, P. Peterson, SciPy: Open source scientific tools for Python (2001). [3] L. Dalcìn, R. Paz, M. Storti, J. D'Elia, MPI for Python: Performance improvements and MPI-2 extensions, Journal of Parallel and Distributed Computing 68 (5) (2008) 655–662. doi:10.1016/j.jpdc.2007.09. 005. [4] A. Collette, Python and HDF5, O'Reilly, 2013. [5] A. Al-Mohy, N. Higham, Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators, SIAM Journal on Scientific Computing 33 (2) (2011) 488–511. doi:10.1137/100788860.

Original language	English
Article number	107724
Journal	Computer Physics Communications
Volume	260
DOIs	https://doi.org/10.1016/j.cpc.2020.107724
Publication status	Published - Mar 2021

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10.1016/j.cpc.2020.107724

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@article{91e79376c8c4425884cc9305c5de0036,

title = "QSW_MPI: A framework for parallel simulation of quantum stochastic walks",

abstract = "QSW_MPI is a Python package developed for time-series simulation of continuous-time quantum stochastic walks. This model allows for the study of Markovian open quantum systems in the Lindblad formalism, including a generalisation of the continuous-time random walk and continuous-time quantum walk. Consisting of a Python interface accessing parallelised Fortran libraries utilising sparse data structures, QSW_MPI is scalable to massively parallel computers, which makes possible the simulation of a wide range of walk dynamics on directed and undirected graphs of arbitrary complexity. Program summary: Program Title: QSW_MPI CPC Library link to program files: http://dx.doi.org/10.17632/r8jsx4xxgv.1 Licensing provisions: GPLv3 Programming language: Python 3 + Fortran 2003 External routines/libraries: NumPy [1], SciPy [2], MPI for Python [3], h5py[4] Nature of problem: QSW_MPI provides a framework for the simulation of quantum stochastic walks on arbitrary graphs (directed/undirected, weighted/unweighted). Solution method: A parallel distributed-memory implementation of the matrix exponential via a truncated Taylor series expansion with scaling and squaring [5]. Additional comments including restrictions and unusual features: QSW_MPI will provide support for the simulation of multiple quantum walkers in a future version. References: [1] T. Oliphant, NumPy: A guide to NumPy, 2006, published: USA: Trelgol Publishing. [2] E. Jones, T. Oliphant, P. Peterson, SciPy: Open source scientific tools for Python (2001). [3] L. Dalc{\`i}n, R. Paz, M. Storti, J. D'Elia, MPI for Python: Performance improvements and MPI-2 extensions, Journal of Parallel and Distributed Computing 68 (5) (2008) 655–662. doi:10.1016/j.jpdc.2007.09. 005. [4] A. Collette, Python and HDF5, O'Reilly, 2013. [5] A. Al-Mohy, N. Higham, Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators, SIAM Journal on Scientific Computing 33 (2) (2011) 488–511. doi:10.1137/100788860.",

keywords = "Lindblad master equation, Markovian dynamics, Open quantum walk, Parallel computation, Quantum stochastic walk",

author = "Edric Matwiejew and Jingbo Wang",

year = "2021",

month = mar,

doi = "10.1016/j.cpc.2020.107724",

language = "English",

volume = "260",

journal = "Computer Physics Communications",

issn = "0010-4655",

publisher = "Elsevier",

}

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AU - Matwiejew, Edric

AU - Wang, Jingbo

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N2 - QSW_MPI is a Python package developed for time-series simulation of continuous-time quantum stochastic walks. This model allows for the study of Markovian open quantum systems in the Lindblad formalism, including a generalisation of the continuous-time random walk and continuous-time quantum walk. Consisting of a Python interface accessing parallelised Fortran libraries utilising sparse data structures, QSW_MPI is scalable to massively parallel computers, which makes possible the simulation of a wide range of walk dynamics on directed and undirected graphs of arbitrary complexity. Program summary: Program Title: QSW_MPI CPC Library link to program files: http://dx.doi.org/10.17632/r8jsx4xxgv.1 Licensing provisions: GPLv3 Programming language: Python 3 + Fortran 2003 External routines/libraries: NumPy [1], SciPy [2], MPI for Python [3], h5py[4] Nature of problem: QSW_MPI provides a framework for the simulation of quantum stochastic walks on arbitrary graphs (directed/undirected, weighted/unweighted). Solution method: A parallel distributed-memory implementation of the matrix exponential via a truncated Taylor series expansion with scaling and squaring [5]. Additional comments including restrictions and unusual features: QSW_MPI will provide support for the simulation of multiple quantum walkers in a future version. References: [1] T. Oliphant, NumPy: A guide to NumPy, 2006, published: USA: Trelgol Publishing. [2] E. Jones, T. Oliphant, P. Peterson, SciPy: Open source scientific tools for Python (2001). [3] L. Dalcìn, R. Paz, M. Storti, J. D'Elia, MPI for Python: Performance improvements and MPI-2 extensions, Journal of Parallel and Distributed Computing 68 (5) (2008) 655–662. doi:10.1016/j.jpdc.2007.09. 005. [4] A. Collette, Python and HDF5, O'Reilly, 2013. [5] A. Al-Mohy, N. Higham, Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators, SIAM Journal on Scientific Computing 33 (2) (2011) 488–511. doi:10.1137/100788860.

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KW - Lindblad master equation

KW - Markovian dynamics

KW - Open quantum walk

KW - Parallel computation

KW - Quantum stochastic walk

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QSW_MPI: A framework for parallel simulation of quantum stochastic walks

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