Quantum computing offers promising prospects over classical approaches, especially when exploring innovative strategies for High Energy Physics simulations. In this study, we present the implementation of a Quantum Generative Adversarial Network designed to simultaneously synthesize gluon-initiated jet images across both electromagnetic calorimeter and hadronic calorimeter detector channels, an essential component for realistic simulations at the Large Hadron Collider. The generated outputs exhibit strong agreement with the original data in terms of energy deposition patterns and successfully retain key underlying features learned during training. This work lays the groundwork for future developments in multi-channel image generation and the extension to quark-initiated jet simulations using quantum computational techniques.

This work explores the application of Quantum Generative Adversarial Networks for High Energy Physics simulations at the LHC. The model is implemented to simultaneously generate gluon-initiated jet images for both ECAL and HCAL detectors. The model successfully reproduces realistic energy deposition patterns while preserving key data features.