论文标题
超越量子噪声光谱：使用量子特征工程进行建模和减轻噪声
Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
论文作者
论文摘要
使用量子技术来实现有用的任务的能力，无论是科学还是与行业相关，都归结为精确的量子控制。通常，由于表征量子系统或设备的困难，很难评估提出的解决方案。这些之所以出现，是因为不可能表征某些成分的原位，并且会因环境和主动控制所引起的噪声而加剧。在这里，我们提出了使用一个由量子特征组成的新型深度学习框架的通用表征和控制解决方案。我们提供框架，示例数据集，训练有素的模型及其性能指标。此外，我们演示了如何使用训练有素的模型来提取常规指标，例如噪声功率谱。
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.
