Hybrid quantum classical graph neural networks for particle track reconstruction
You can cite this article as;
Tüysüz, C., Rieger, C., Novotny, K. et al. Hybrid quantum classical graph neural networks for particle track reconstruction. Quantum Mach. Intell. 3, 29 (2021). https://doi.org/10.1007/s42484-021-00055-9
First, please refer to our installation guide to setup the necessary tools.
Use train.py to train a model.
Models are available in qnetworks folder.
Choose the model and other hyperparameters using a configuration
file (see configs folder for examples).
Execute the following to train a model.
python3 train.py [PATH-TO-CONFIG-FILE] 1 or use the following to train multiple instances in parallel.
source send_jobs_multiple.sh [PATH-TO-CONFIG-FILE] [NUM_RUNS]