Graph Decoder

Graph neural network decoder for the rotated surface code.

This repository accompanies the article:

Data-driven decoding of quantum error correcting codes using graph neural networks

If you use this code in your research, please cite:

@article{lange2025data,

title={Data-driven decoding of quantum error correcting codes using graph neural networks},

author={Lange, Moritz and Havstr{"o}m, Pontus and Srivastava, Basudha and Bengtsson, Isak and Bergentall, Valdemar and Hammar, Karl and Heuts, Olivia and van Nieuwenburg, Evert and Granath, Mats},

journal={Physical Review Research},

volume={7},

number={2},

pages={023181},

year={2025},

publisher={APS} }

Includes the source code for the GNN decoder, as well as scripts to run the code on a cluster.

Getting started

Follow these steps to run the code on a cluster.

• Clone the repository with git clone https://github.com/LangeMoritz/GNN_decoder
• Install the required packages in requirements.txt, for example using a virtual environment:

python3 -m venv .venv 
source .venv/bin/activate
python -m pip install -r requirements.txt

• Install PyTorch by following the instructions found here.
• Install PyTorch Geometric by following the instructions found here.

Repository structure

• src/ contains the "source code" for the project, split into a number of modules:
  • decoder.py A class for creating a decoder object with methods for training a GNN decoder and running decoding simulations.
  • gnn_models.py Contains PyTorch geometric graph neural network decoder models.
  • graph_representation.py Functions for creating syndromes graphs.
  • utils.py Function for reading argument files.

├── src
│   ├── decoder.py
│   ├── gnn_models.py
│   ├── graph_representation.py
│   └── utils.py

• models/ contains trained models corresponding to fig. 3 (circuit_level_noise/), fig. 5 and 6 (repetition_code/) and fig. 7 (perfect_stabilizers/)
• run_training.sh is the shell run script used to start is jobs for train_nn.py.
• .gitignore lists files and directories in the git repository to be ignored in commits.
• requirements.txt lists the required python packages. See the Getting Started section above.