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

TL;DR

This paper presents a machine learning-based method to classify classical noise correlations affecting a two-qubit quantum sensor, achieving high accuracy with minimal experimental effort.

Contribution

It introduces a novel AI-assisted protocol for distinguishing different types of noise correlations in quantum systems using two qubits as probes.

Findings

Achieves around 90% classification accuracy.

Uses minimal experimental measurements.

Effective for different noise classes and conditions.

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.