In this notebook a quantum Generative Adversarial Network is trained for the simulation of a calorimeter module of the LHCb experiment. The calorimeter module measure energy deposits as images of 8x8 pixels. The discriminator is a classical neural network and the generator is a variational quantum circuit. Several figures of merit are studied to validate the simulation, as the Kolmogorov-Smirnov test for the distribution of total measured energy, and the RSME test compatibility of calorimeter images.