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Torontonian Sampling

This repository contains the source code used to produce the results presented in "Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer" arXiv:1810.00900.

Contents

This repository contains:

  • Fortran source code in the directory src which calculates samples from the Torontonian, given a Gaussian state vector of means and covariance matrix.
  • This Fortran source code can also be interfaced with Python
  • Two examples on how to use the Torontonian sampling module. A Fortran example in the examples folder, and an interactive Python Jupyter notebook TorontonianSampling.ipynb.

Installation

The Torontonian sampling Fortran module can be used either via Fortran, or via the Python interface.

Interfacing via Fortran

If using the module via Fortran, no external dependencies are required, just a Fortran compiler like gfortran. On Ubuntu based systems, this can be installed via apt:

sudo apt install gfortran

Then, simply run

make fortran

in the top level directory. The Fortran modules will be compiled, and the modules stored in the directory include. To use the module with your own Fortran, simply include the use torontonian_samples at the top of the program, and compile the commands

gfortran -o program program.f90  /path/to/include/*.o -I/path/to/include/

See the file examples/fortran_example.f90 for an example program that uses the Torontonian sampling module. This can be compiled by running make example from the top level directory.

Interfacing via Python

To compile the module for use with Python, NumPy is required to be installed, as well as a Fortran compiler such as gfortran. NumPy can be installed via pip:

pip install numpy

Then, simply run

make python

in the top level directory to compile the Python module. The module torontonian_samples.cpython-*-.so will be created, which can then be imported in Python via import torontonian_samples.

Authors

Brajesh Gupt.

If you are doing any research using this source code, please cite the following paper:

Brajesh Gupt, Juan Miguel Arrazola, Nicolas Quesada, and Thomas R. Bromley. Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer, arXiv:1810.00900

License

This source code is free and open source, released under the Apache License, Version 2.0.