This repository contains the randomly generated graphs used in the work Minor Embedding for Quantum Annealing with Reinforcement Learning by Riccardo Nembrini, Maurizio Ferrari Dacrema and Paolo Cremonesi.
The dataset includes training and testing sets of randomly generated graphs, stored in .zip files located in the dataset directory. These files contain NetworkX graphs saved in the edgelist format.
To extract and prepare the data for use, run the following command:
python extract_data.py
Make sure to run this command from the root directory of the repository. The command will extract the data into two directories, training and testing, within the dataset folder.
We also provide a Jupyter Notebook, load_data.ipynb, which demonstrates how to load and analyze the two graph sets, as well as how to visualize the graphs.
The notebook includes key information about the graphs. If you wish to run it locally, you will need to install the necessary libraries, which are listed in the requirements.txt file.
To set up your environment, you can create a new Python environment using virtualenv, conda, or other tools. Then, install the required libraries using pip or your preferred package manager:
python -m venv .venv
source .venv/bin/activate # On Windows, use .venv\Scripts\activate
pip install -r requirements.txt
jupyter notebook
Once the environment is set up, you can open and run the notebook to explore the data and graphs.