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.
@article{arxiv.2512.24135,
title = {Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor},
author = {Dario Fasone and Shreyasi Mukherjee and Mauro Paternostro and Elisabetta Paladino and Luigi Giannelli and Giuseppe A. Falci},
journal= {arXiv preprint arXiv:2512.24135},
year = {2026}
}