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.
Noise fingerprints in quantum computers: Machine learning software tools / Martina, Stefano; Gherardini, Stefano; Buffoni, Lorenzo; Caruso, Filippo. - In: SOFTWARE IMPACTS. - ISSN 2665-9638. - ELETTRONICO. - 12:(2022), pp. 0-0. [10.1016/j.simpa.2022.100260]
Noise fingerprints in quantum computers: Machine learning software tools
Martina, Stefano;Gherardini, Stefano;Buffoni, Lorenzo;Caruso, Filippo
2022
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.
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