The Quantum Angle Generator (QAG) constitutes a new quantum machine learning model designed to generate accurate images on current Noise Intermediate Scale (NISQ) Quantum devices. Variational quan- tum circuits constitute 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, it is the first time that such accurate results are achieved by a quantum model. To explore the noise robustness of the model, an extensive quan- tum noise study is carried out. In this paper it is demonstrated, that the model trained on the quantum device learns the hardware noise behaviour and generates outstanding results with it. 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 to a crucial high energy physics simulation use case. The sim- ulations are required to measure particle energies and, ultimately, to discover unknown particles at the Large Hadron Collider at CERN.
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