Learning the noise fingerprint of quantum devices
Year: 2022
Authors: Martina S., Buffoni L., Gherardini S., Caruso F.
Autors Affiliation: Department of Physics and Astronomy, University of Florence, Via Sansone, 1, FI, Sesto Fiorentino, I-50019, Italy; European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara 1, FI, Sesto Fiorentino, I-50019, Italy; European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara 1, FI, Sesto Fiorentino, I-50019, Italy; Physics of Information and Quantum Technologies Group, Instituto de Telecomunicazkhes, University of Lisbon, Av. Rovisco Pais, Lisbon, P-1049-001, Portugal; Physics of Information and Quantum Technologies Group, Instituto de Telecomunicazkhes, University of Lisbon, Av. Rovisco Pais, Lisbon, P-1049-001, Portugal; CNR-INO & Scuola Internazionale Superiore di Studi Avanzati (SISSA), TS, Trieste, Italy
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.
Journal/Review: QUANTUM MACHINE INTELLIGENCE
Volume: 4 (1) Pages from: 8-1 to: 8-12
More Information: This work was financially supported by Fondazione CR Firenze through the project QUANTUM-AI, by University of Florence through the project Q-CODYCES, and by the European Union´s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS).KeyWords: noise fingerprint, quantum computer, machine learningDOI: 10.1007/s42484-022-00066-0