Noise fingerprints in quantum computers: Machine learning software tools

TL;DR

This paper introduces a machine learning software tool that identifies and classifies noise fingerprints in quantum computers with high accuracy, aiding in understanding and mitigating quantum noise effects.

Contribution

The paper presents a novel software architecture that effectively classifies quantum noise fingerprints across different devices and time dependencies with over 99% accuracy.

Findings

Achieved over 99% classification accuracy of noise fingerprints.

Successfully distinguished noise sources across different quantum devices.

Identified temporal variations in noise fingerprints within single quantum machines.

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.