Noise fingerprints in quantum computers: Machine learning software tools
arXiv:2202.04581 · doi:10.1016/j.simpa.2022.100260
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.
9 pages, 2 figures
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