The quantum angle generator (QAG) is a new full quantum machine learning model designed to generate accurate images on current noise intermediate scale 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.

中文翻译：

---

在当前嘈杂的量子计算设备上精确生成图像


量子角度生成器（QAG）是一种新的全量子机器学习模型，旨在在当前噪声中等规模量子设备上生成准确的图像。变分量子电路构成了 QAG 模型的核心，并对各种电路架构进行了评估。结合所谓的 MERA 上采样架构，QAG 模型取得了优异的结果，对此进行了详细的分析和评估。据我们所知，这是量子模型首次取得如此准确的结果。为了探索模型对噪声的鲁棒性，进行了广泛的量子噪声研究。本文证明，在物理量子设备上训练的模型可以学习硬件的噪声特性并产生出色的结果。经验证，即使训练期间量子硬件机器校准变化高达 8%，也能得到很好的容忍。为了进行演示，该模型被用于高能物理中不可或缺的模拟，这些模拟需要测量粒子能量，并最终在欧洲核子研究中心的大型强子对撞机中发现未知粒子。