Noise fingerprints in quantum computers: Machine learning software tools
Quantum Physics
2022-03-30 v1 Machine Learning
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.
Cite
@article{arxiv.2202.04581,
title = {Noise fingerprints in quantum computers: Machine learning software tools},
author = {Stefano Martina and Stefano Gherardini and Lorenzo Buffoni and Filippo Caruso},
journal= {arXiv preprint arXiv:2202.04581},
year = {2022}
}
Comments
9 pages, 2 figures