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.
Learning the noise fingerprint of quantum devices / Martina S.; Buffoni L.; Gherardini S.; Caruso F.. - In: QUANTUM MACHINE INTELLIGENCE. - ISSN 2524-4906. - ELETTRONICO. - 4:(2022), pp. 1-12. [10.1007/s42484-022-00066-0]
Learning the noise fingerprint of quantum devices
Martina S.;Buffoni L.;Gherardini S.;Caruso F.
2022
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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