Quantum Generative Adversarial Networks for High Energy Physics Simulations

The potential for quantum computing to offer significant advantages over classicalcomputing makes it a promising approach for exploring alternative future methodsin High Energy Physics (HEP) simulations. This work presents the implementationof a Quantum Generative Adversarial Network (qGAN) to generate gluon-initiatedjet images from ECAL detector data, a task crucial for high-energy physics simula-tions at the Large Hadron Collider (LHC). The results demonstrate high fidelityin replicating energy deposit patterns and preserving the implicit training datafeatures. This study marks the first step toward generating multi-channel picturesand quark-initiated jet images.