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Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor

arXiv
Authors: Dario Fasone, Shreyasi Mukherjee, Mauro Paternostro, Elisabetta Paladino, Luigi Giannelli, Giuseppe A. Falci

Year

2025

Paper ID

36081

Status

Preprint

Abstract Read

~2 min

Abstract Words

77

Citations

N/A

Abstract

We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting qubits as probe. We consider different classes of noise, according to their Markovianity and spatial correlations. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various noises are discriminated by only measuring the final transfer efficiencies. This approach reaches around 90% accuracy with a minimal experimental overhead.

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  • We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting...

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