Contains code for https://arxiv.org/abs/1912.12919
Deep Q-learning decoder for depolarizing noise on the toric code
David Fitzek, Mattias Eliasson, Anton Frisk Kockum, Mats Granath
Deep reinforcement learning decoder for the toric code in pytorch.
- Python 3
- The required libraries are matplotlib, numpy and pytorch
pip install -r requirements.txt- Clone this repo:
git clone https://github.com/mats-granath/toric-RL-decoder.gitThere are two example scripts
- train_script.py
- prediction_script.py
The train script trains an agent to solve syndromes. All the hyperparameters related to the training are specified in the script. Moreover, an evaluation of the training run is stored in the data folder with a timestamp.
The predict script uses a trained network and predicts given a specified amount of syndromes. The trained network can be loaded from the network folder.
| File | Description |
|---|---|
├── data |
A directory that contains the evaluation data for each training or prediction run. |
├── docs |
Documentation for the different classes and functions. |
├── network |
Pretrained models for the grid sizes 5,7, and 9. |
├── plots |
All the plots generated during prediction are saved in that folder. |
├── src |
Source files for the agent and toric code. |
· ├── RL.py |
|
· ├── Replay_memory.py |
Contains classes for a replay memory with uniform and proportional sampling. |
· ├── Sum_tree.py |
A binary tree data structure where the parent’s value is the sum of its children. |
· └── Toric_model.py |
Contains the class toric_code and functions that are relevant to manipulate the grid. |
├── NN.py |
Contains different network architectures |
├── README.md |
About the project. |
├── ResNet.py |
Contains different ResNet architectures |
├── requirements.txt |
The Python libraries that will be installed. Only the libraries in this official repo will be available. |
├── train_script |
A script training an agent to solve the toric code. |
└── prediction_script |
The trained agent solves syndromes. |