The ability to use quantum technology to achieve useful tasks, be they scientific or industry related, boils down to precise quantum control. In general it is difficult to assess a proposed solution due to the difficulties in characterizing the quantum system or device. These arise because of the impossibility to characterize certain components in situ, and are exacerbated by noise induced by the environment and active controls. Here, we present a general purpose characterization and control solution making use of a deep learning framework composed of quantum features. We provide the framework, sample datasets, trained models, and their performance metrics. In addition, we demonstrate how the trained model can be used to extract conventional indicators, such as noise power spectra.

利用量子技术完成有用的任务的能力，无论是科学方面的还是与行业相关的，都归结为精确的量子控制。通常，由于难以表征量子系统或器件，因此难以评估提出的解决方案。这些是由于无法原位表征某些组件而引起的，并且由于环境和主动控件引起的噪声而加剧。在这里，我们提出了利用由量子特征组成的深度学习框架的通用表征和控制解决方案。我们提供了框架，样本数据集，训练有素的模型及其性能指标。此外，我们演示了如何将训练后的模型用于提取常规指标，例如噪声功率谱。