qLDPC Decoding

This repository contains code that explores decoding circuit level noise for bivariate bicyle codes, as well as surface codes.

In order to run the code in this environment, create a conda environment with the following command (this will result in a cpu install of pytorch):

conda env create --f environment_cpu.yml

This creates a conda environment named qLDPC-decoding.

If you have access to a Linux machine with an Nvidia gpu (with support for cuda 11.8), you can create a conda environment with a cuda install of pytorch with

conda env create --f environment_gpu.yml

To run tests, simply run

pytest

from the root directory of the repository.

Training models is performed with the train.py script. To see the available options for running this script, run python train.py --help. In order to evaluate a saved model, make sure to save the std out from the train.py script to a file. The name of this log file is used as an input for the eval_model.py script.