Quantum generative adversarial networks for gluon initiated jets generation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916645787664384 |
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| author | Guadarrama, Rey Gleyzer, Sergei Baidachna, Mariia Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia Pedraza, Isabel Dahale, Gopal Ramesh Hernández-Arellano, Haydee |
| author_facet | Guadarrama, Rey Gleyzer, Sergei Baidachna, Mariia Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia Pedraza, Isabel Dahale, Gopal Ramesh Hernández-Arellano, Haydee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum generative adversarial networks for gluon initiated jets generation Guadarrama, Rey Gleyzer, Sergei Baidachna, Mariia Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia Pedraza, Isabel Dahale, Gopal Ramesh Hernández-Arellano, Haydee Computational Physics High Energy Physics - Phenomenology 81P68, 68T05, 62P35, 82B80 I.2.6; I.5.1; G.3; J.2 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. |
| title | Quantum generative adversarial networks for gluon initiated jets generation |
| topic | Computational Physics High Energy Physics - Phenomenology 81P68, 68T05, 62P35, 82B80 I.2.6; I.5.1; G.3; J.2 |
| url | https://arxiv.org/abs/2503.05044 |