TL;DR
This paper demonstrates how machine learning can identify and characterize the unique noise fingerprints of IBM quantum computers, revealing their dependence on physical platform and temporal evolution.
Contribution
It introduces a method to experimentally identify and classify noise fingerprints of quantum devices using machine learning on outcome probability sequences.
Findings
Successfully classified noise distributions of IBM quantum computers
Revealed that noise fingerprints depend on the physical platform
Showed that noise characteristics evolve over time
Abstract
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.
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