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https://github.com/xanaduai/torontonian-sampling
This repository contains the source code used to produce the results presented in the paper "Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer".
https://github.com/xanaduai/torontonian-sampling
algorithm graphs graphs-theory matrix optimization optimization-algorithms physics quantum quantum-computing
Last synced: 14 days ago
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This repository contains the source code used to produce the results presented in the paper "Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer".
- Host: GitHub
- URL: https://github.com/xanaduai/torontonian-sampling
- Owner: XanaduAI
- License: apache-2.0
- Created: 2018-09-27T06:17:48.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-26T16:17:33.000Z (almost 6 years ago)
- Last Synced: 2024-11-06T17:49:15.012Z (2 months ago)
- Topics: algorithm, graphs, graphs-theory, matrix, optimization, optimization-algorithms, physics, quantum, quantum-computing
- Language: Fortran
- Homepage: https://arxiv.org/abs/1810.00900
- Size: 26.4 KB
- Stars: 9
- Watchers: 7
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://arxiv.org/abs/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:
```bash
sudo apt install gfortran
```
Then, simply run
```bash
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
```bash
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`:
```bash
pip install numpy
```
Then, simply run
```bash
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](https://arxiv.org/abs/1810.00900)
## License
This source code is free and open source, released under the Apache License, Version 2.0.