  The prospect of quantum computing with a potential exponential speed-up
compared to classical computing identifies it as a promising method in the
search for alternative future High Energy Physics (HEP) simulation approaches.
HEP simulations, such as employed at the Large Hadron Collider at CERN, are
extraordinarily complex and require an immense amount of computing resources in
hardware and time. For some HEP simulations, classical machine learning models
have already been successfully developed and tested, resulting in several
orders of magnitude speed-up. In this research, we proceed to the next step and
explore whether quantum computing can provide sufficient accuracy, and further
improvements, suggesting it as an exciting direction of future investigations.
  With a small prototype model, we demonstrate a full quantum Generative
Adversarial Network (GAN) model for generating downsized eight-pixel
calorimeter shower images. The advantage over previous quantum models is that
the model generates real individual images containing pixel energy values
instead of simple probability distributions averaged over a test sample.
  To complete the picture, the results of the full quantum GAN model are
compared to hybrid quantum-classical models using a classical discriminator
neural network.
