Learning the noise fingerprint of quantum devices

Year: 2022

Authors: Martina S., Buffoni L., Gherardini S., Caruso F.

Autors Affiliation: Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, FI, Italy; Univ Florence, European Lab Non Linear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, FI, Italy; Univ Lisbon, Inst Telecomun, Phys Informat & Quantum Technol Grp, Av Rovisco Pais, P-1049001 Lisbon, Portugal; CNR INO, Trieste, Italy; Scuola Int Super Avanzati SISSA, 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 learning
DOI: 10.1007/s42484-022-00066-0

Citations: 12
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