  The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning
model designed to generate accurate images on current Noise Intermediate Scale
(NISQ) Quantum devices. Variational quantum circuits form the core of the QAG
model, and various circuit architectures are evaluated. In combination with the
so-called MERA-upsampling architecture, the QAG model achieves excellent
results, which are analyzed and evaluated in detail. To our knowledge, this is
the first time that a quantum model has achieved such accurate results. To
explore the robustness of the model to noise, an extensive quantum noise study
is performed. In this paper, it is demonstrated that the model trained on a
physical quantum device learns the noise characteristics of the hardware and
generates outstanding results. It is verified that even a quantum hardware
machine calibration change during training of up to 8% can be well tolerated.
For demonstration, the model is employed in indispensable simulations in high
energy physics required to measure particle energies and, ultimately, to
discover unknown particles at the Large Hadron Collider at CERN.
