Skip to content

Latest commit

 

History

History
39 lines (23 loc) · 1.11 KB

README.md

File metadata and controls

39 lines (23 loc) · 1.11 KB

Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

Publication:

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

How to use?

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]