Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is realized, in the form of a distinguishable fingerprint. Noise sources are also expected to evolve and change over time. Here, we first identify and then characterize experimentally the noise fingerprint of IBM cloud-available quantum computers, by resorting to machine learning techniques designed to classify noise distributions using time-ordered sequences of measured outcome probabilities.
翻译:噪音源不可避免会影响任何量子技术装置。 噪音的主要特征预计将严格取决于实现量子装置的物理平台, 其形式是可辨别的指纹。 噪音源也会随着时间变化和变化。 在这里, 我们首先通过采用机器学习技术, 利用时间顺序的测算结果概率序列对噪音分布进行分类, 从而对IBM云中可用量子计算机的噪音指纹进行实验性鉴定和定性。