Detection of noise correlations in two qubit systems by Machine Learning

• URL: http://arxiv.org/abs/2509.03389v1
• Date: Wed, 03 Sep 2025 15:13:01 GMT
• Title: Detection of noise correlations in two qubit systems by Machine Learning
• Abstract summary: We introduce and validate a machine-learning assisted quantum sensing protocol to classify spatial and temporal correlations of classical noise.<n>We consider six distinct classes of Markovian and non-Markovian noise.<n>Our approach achieves $gtrsim 86%$ accuracy in classification providing a near-perfect discrimination between Markovian and non-Markovian noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce and validate a machine-learning assisted quantum sensing protocol to classify spatial and temporal correlations of classical noise affecting two ultrastrongly coupled qubits. We consider six distinct classes of Markovian and non-Markovian noise. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various forms of noise are discriminated by only measuring the final transfer efficiencies. Our approach achieves $\gtrsim 86\%$ accuracy in classification providing a near-perfect discrimination between Markovian and non-Markovian noise. The method requires minimal experimental resources, relying on a simple driving scheme providing three inputs to a shallow neural network with no need of measuring time-series data or real-time monitoring. The machine-learning data analysis acquires information from non-idealities of the coherent protocol highlighting how combining these techniques may significantly improve the characterization of quantum-hardware.

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