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An MPI enabled CTQW simulator

Project description

Fortran library and Python module to calculate continuous-time quantum walks

This is intended to provide a framework to quickly and easily work with quantum walkers, take advantage of high performance computing, and allow easy visualisation.

For more information on how to use this package, please see the online documentation


  • Fortran and Python bindings (in the form of a library and module respectively)

  • Supports MPI through the use of the PETSc and SLEPc high-performance sparse matrix libraries (CUDA support planned)

  • Has in-built support for infinite-line hamiltonians

  • Import and export matrices/states in binary or text

  • Can import custom adjancency matrices

  • Supports both one and two walkers (non-interacting)

  • Import and export matrices/states in binary, text or matlab format

  • Python module supports plotting and visualisation using matplotlib and networkx

  • Entanglement calculations

  • Ability to place diagonal defects/barriers on graph vertices

  • MPI graph isomorphism methods, for both 2 and 3 interacting particles


  • CUDA support

  • Calculation of transmission

  • Add fallback modes when PETSc/SLEPc are not present


In addition to an MPI implementation (e.g. MPICH or Open MPI), the python pyCTQW.MPI module depends on the following components:

Installation using pip

After ensuring NumPy and petsc4py are installed (and all PETSc, SLEPc and MPI environment variables are properly set), pyCTQW can be installed using pip:

$ pip install pyCTQW

Installation from source code

Alternatively, the source code can be downloaded and compiled manually:

  1. Ensure all dependencies required above are installed

  2. Extract the pyCTQW folder, and cd into the extracted directory:

    $ tar xvzf pyctqw-1.1.0.tar.gz
    $ cd pyctqw-1.1.0
  3. Ensure that your PETSc and SLEPc environment variables are correctly set; for example,

    $ export PETSC_DIR=/path/to/petsc
    $ export PETSC_ARCH=linux-gnu
    $ export SLEPC_DIR=/path/to/slepc

    If you are unsure what your PETSc or SLEPc variables should be, please refer to their documentation.

  4. Compile the Python module pyCTQW.MPI by running

    $ python build
  5. System-wide install:

    $ sudo -E python install

    where the command -E ensures that the environment variables set in step 3 are passed to the root.

    Now, have a go running some of the examples!

Optional: compiling libctqwMPI

In addition to an MPI implementation (e.g. MPICH or Open MPI), the Fortran library libctqwMPI depends on the following components: - PETSc >= 3.4.2 - SLEPc >= 3.4.1

Once these dependencies are installed, simply open a terminal in the root directory of pyCTQW-X.Y and run

$ make fortran [options]

where available options include





0 (default), 1

whether to build libctqwMPI as a shared library (shared_lib=1, producing or a static library (shared_lib=0 (default), producing libctqwMPI.a).

If built as a shared library, compiled programs will be smaller, but will need to be added to a directory used by ld (either by setting the environment variable LD_LIBRARY_PATH or by placing in /usr/local/lib etc).

The fortran library ( or libctqwMPI.a) can be found in the pyCTQW-X.Y/lib directory, with required module files found in the pyCTQW-X.Y/include directory.

Optional: build documentation

If Sphinx is installed, the documentation can be compiled by running

$ pip install -r docs/requirements.txt
$ make docs


For more information on how to use this package, please see the online documentation


The graph isomorphism subroutine GraphISCert uses the external subroutine d_refsor, a highly optimised Fortran sorting implementation written by Michel Olagnon and part of the ORDERPACK 2.0 suite of ranking and sorting algorithms for Fortran 90.

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