Paper ID: 2202.04581

Noise fingerprints in quantum computers: Machine learning software tools

Stefano Martina, Stefano Gherardini, Lorenzo Buffoni, Filippo Caruso

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.

Submitted: Feb 9, 2022