Quantum generative adversarial networks for gluon initiated jets generation

Rey Guadarrama; Sergei Gleyzer; Mariia Baidachna; Kyoungchul Kong; Konstantin T. Matchev; Katia Matcheva; Isabel Pedraza; Gopal Ramesh Dahale; Haydee Hernández-Arellano

arXiv · 2503.05044

Quantum generative adversarial networks for gluon initiated jets generation

Rey Guadarrama·Sergei Gleyzer·Mariia Baidachna·Kyoungchul Kong·Konstantin T. Matchev·Katia Matcheva·Isabel Pedraza·Gopal Ramesh Dahale·Haydee Hernández-Arellano

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Abstract

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.

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Rey Guadarrama, Sergei Gleyzer, Mariia Baidachna, Kyoungchul Kong, Konstantin T. Matchev, Katia Matcheva, Isabel Pedraza, Gopal Ramesh Dahale, Haydee Hernández-Arellano. 2025-03-06. Quantum generative adversarial networks for gluon initiated jets generation. https://arxiv.org/abs/2503.05044

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