Noise fingerprints in quantum computers: Machine learning software tools
Year: 2022
Authors: Martina S.; Gherardini S.; Buffoni L.; Caruso F.
Autors Affiliation: Department of Physics and Astronomy, University of Florence, Via Sansone 1, Sesto Fiorentino, I-50019, Italy; European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara 1, Sesto Fiorentino, I-50019, Italy; European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara 1, 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, Area Science Park, Strada Statale 14, Basovizza (TS), I-34149, Italy; Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea, 265, Trieste, I-34136, Italy
Abstract: In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.
Journal/Review: SOFTWARE IMPACTS
Volume: 12 Pages from: to:
KeyWords: noise fingerprints, quantum computers, machine learning, softwareDOI: 10.1016/j.simpa.2022.100260ImpactFactor: 2.100
