Numerical simulations of collision events within the ATLAS experiment have substantially contributed to the design of future experiments and, particularly, to analyzing ongoing ones. However, the accuracy achieved in describing Large Hadron Collider (LHC) collisions comes at a substantial computational cost, with projections estimating the requirement of millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run. Notably, the full simulation of a single LHC event using Geant4 currently demands approximately 1000 CPU seconds, with calorimeter simulations dominating the computational burden. Deep generative models are being developed to act as surrogates of the calorimeter data generation pipeline, and can potentially decrease the overall time to simulate single events by orders of magnitude. We introduce a pipeline that combines deep generative models with quantum annealers for calorimeter shower generation. Our model combines a variational autoencoder (VAE) on the exterior with a Restricted Boltzmann Machine (RBM) in the latent space. RBM nodes and connections are crafted to enable the use of qubits and couplers on D-Wave's Zephyr Quantum Annealer. We test our pipeline using one of the CaloChallenge datasets and compare it with other deep generative frameworks.