Noise fingerprints in quantum computers: Machine learning software tools
This work addresses noise characterization in quantum computing, which is crucial for improving device reliability, but it appears incremental as it focuses on software tools rather than novel methods.
The paper tackles the problem of identifying quantum noise sources in quantum computers by developing a machine learning software tool that classifies noise fingerprints with over 99% accuracy across devices or over time.
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.