Quantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics (HEP) simulations. This work presents the implementation of a Quantum Generative Adversarial Network (qGAN) to simultaneously generate gluon-initiated jet images for both ECAL and HCAL detector channels, a task crucial for high-energy physics simulations 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 using quantum computing.
@article{arxiv.2503.05044,
title = {Quantum generative adversarial networks for gluon initiated jets generation},
author = {Rey Guadarrama and Sergei Gleyzer and Mariia Baidachna and Kyoungchul Kong and Konstantin T. Matchev and Katia Matcheva and Isabel Pedraza and Gopal Ramesh Dahale and Haydee Hernández-Arellano},
journal= {arXiv preprint arXiv:2503.05044},
year = {2025}
}
Comments
This paper was presented at the Quantum Computing and Artificial Intelligence Conference 2025 (QCAI 2025) [https://sites.google.com/view/qcai2025]