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

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2512.24135

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

Institutions: University of Catania, University of Naples Federico II, University of Palermo, Istituto Nazionale di Fisica Nucleare, Sezione di Catania

Topic: Quantum Information and Cryptography

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Department for the Economy, European Commission, Università di Catania, HORIZON EUROPE Framework Programme

Awards: G1365187887, G1954133636, G5443090575, G5677546062, G6821267586, G8743931066, G972786147

OpenAlex ID: W7117974296

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