Quantum Generative Adversarial Networks for High Energy Physics Simulations
Abstract
The potential for quantum computing to offer significant advantages over classical computing makes it a promising approach for exploring alternative future methods in High Energy Physics (HEP) simulations. This work presents the implementation of a Quantum Generative Adversarial Network (qGAN) to generate gluon-initiated jet images from ECAL detector data, a task crucial for high-energy physics simula- tions at the Large Hadron Collider (LHC). The results demonstrate high fidelity in replicating energy deposit patterns and preserving the implicit training data features. This study marks the first step toward generating multi-channel pictures and quark-initiated jet images.
Cite
Text
Guadarrama et al. "Quantum Generative Adversarial Networks for High Energy Physics Simulations." NeurIPS 2024 Workshops: MLNCP, 2024.Markdown
[Guadarrama et al. "Quantum Generative Adversarial Networks for High Energy Physics Simulations." NeurIPS 2024 Workshops: MLNCP, 2024.](https://mlanthology.org/neuripsw/2024/guadarrama2024neuripsw-quantum/)BibTeX
@inproceedings{guadarrama2024neuripsw-quantum,
title = {{Quantum Generative Adversarial Networks for High Energy Physics Simulations}},
author = {Guadarrama, Rey and Gleyzer, Sergei and Matchev, Konstantin and Matcheva, Katia and Kong, Kyoungchul and Dahale, Gopal Ramesh and Baidachna, Mariia and Hernández-Arellano, Haydee and Pedraza, Isabel},
booktitle = {NeurIPS 2024 Workshops: MLNCP},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/guadarrama2024neuripsw-quantum/}
}