The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. The study demonstrates the QAG model's ability to learn hardware noise behavior, with stable results in the presence of simulated quantum hardware noise up to $1.5\%$ during inference and $3\%$ during training. However, deploying the noiseless trained model on real quantum hardware reduces accuracy. Training the model directly on hardware allows it to learn the underlying noise behavior, maintaining precision comparable to the noisy simulator. The QAG model's noise robustness and accuracy make it suitable for analyzing simulated calorimeter shower images used in high-energy physics simulations at CERN's Large Hadron Collider.