无需复杂神经网络，用量子特征空间高效识别量子噪声类型。
Quantum Feature Space of a Qubit Coupled to an Arbitrary Bath
- 构建量子特征空间，用欧氏距离表征噪声差异
- 仅用随机森林即可准确分类噪声平稳性与类型
- 适合量子控制优化与实时噪声诊断场景
传统量子比特控制依赖于对量子比特-环境耦合的功率谱密度表征。先前工作采用结合深度神经网络与物理编码层的灰箱方法推断描述经典环境影响的噪声算符，但整体结构复杂，难以扩展且不适用于实时操作。本文表明，无需昂贵的神经网络，该噪声算符描述可被高效参数化，由此形成的参数空间称为量子比特动力学的量子特征空间。我们证明，在给定控制集下，量子特征空间中的欧氏距离能有效区分不同噪声过程。将量子特征空间作为简单机器学习算法（此处为随机森林）的输入，可有效分类噪声的平稳性及主要类型。最后，我们研究了控制脉冲参数在量子特征空间中的映射关系。
原文摘要 · Abstract (English)
Qubit control protocols have traditionally leveraged a characterisation of the qubit-bath coupling via its power spectral density. Previous work proposed the inference of noise operators that characterise the influence of a classical bath using a grey-box approach that combines deep neural networks with physics-encoded layers. This overall structure is complex and poses challenges in scaling and real-time operations. Here, we show that no expensive neural networks are needed and that this noise operator description admits an efficient parameterisation. We refer to the resulting parameter space as the \textit{quantum feature space} of the qubit dynamics resulting from the coupled bath. We show that the Euclidean distance defined over the quantum feature space provides an effective method for classifying noise processes in the presence of a given set of controls. Using the quantum feature space as the input space for a simple machine learning algorithm (random forest, in this case), we demonstrate that it can effectively classify the stationarity and the broad class of noise processes perturbing a qubit. Finally, we explore how control pulse parameters map to the quantum feature space.
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