超越量子噪声光谱学：用量子特征工程建模和减轻噪声
Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
摘要：
使用量子技术实现有用任务的能力，无论是科学还是工业相关，都可以归结为精确的量子控制。通常，由于难以表征量子系统或设备，因此难以评估所提出的解决方案。这些问题的出现是因为不可能在现场安装某些部件，并且由于环境和主动控制引起的噪音而加剧。在这里，我们提出了一种通用的表征和控制解决方案，利用由量子特征组成的新型深度学习框架。我们提供了框架，样本数据集，训练模型及其性能指标。此外，我们演示了如何训练模型可以用来提取传统的指标，如噪声功率谱。
英文摘要：
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 characterising the quantum system or device. These arise because of the impossibility to characterise certain components in situ, and are exacerbated by noise induced by the environment and active controls. Here we present a general purpose characterisation and control solution making use of a novel deep learning framework composed of quantum features. We provide the framework, sample data sets, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
