Learning the noise fingerprint of quantum devices
This work addresses noise characterization for quantum computing platforms, which is incremental as it applies existing machine learning methods to a new domain.
The researchers tackled the problem of characterizing noise in quantum devices by identifying and experimentally characterizing the noise fingerprint of IBM cloud-available quantum computers, using machine learning techniques to classify noise distributions from time-ordered sequences of measured outcome probabilities.
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.