Elaris Computing Nexus
Performance Comparison of Full and Hybrid QGANs for Particle Shower Simulation
Elaris Computing Nexus
Received On : 02 June 2026
Revised On : 28 July 2026
Accepted On : 20 August 2026
Published On : 02 September 2026
Volume 02, 2026
Pages : 167-176
Abstract
The use of quantum generative adversarial networks (qGANs) is based on an efficient approach that simulates high-energy particle showers using a reduced model complexity. In this study, we analyze complete and hybrid qGAN architectures for the creation of particle shower images and compare their training behavior, convergence and accuracy. The results show a stable training of the entire qGAN, which can generate the general parameters of the shower well. The hybrid design also exhibits competitive performance with the medium discriminator yielding the highest accuracy of all the tested configurations. However, with a lower or higher discriminator the decrease of performance is due to the imbalance between the capacity of the generator and discriminator. The results show that quantum models can be represented with a much smaller number of trainable parameters, and still capture the representational capabilities of classical models, something that can be studied in greater detail with larger models and more sophisticated physics-based metrics.
Keywords
Quantum Generative Adversarial Networks, Particle Shower Simulation, Hybrid Quantum-Classical Models, Quantum Machine Learning, Generative Models, High-Energy Physics.
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Cite this Article
Bancy Mbura Maranga, “Performance Comparison of Full and Hybrid QGANs for Particle Shower Simulation”, Elaris Computing Nexus, pp. 167-176, 2026, doi: 10.65148/ECN/2026014.
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© 2026 Bancy Mbura Maranga. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.