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arXiv:2512.24393

Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise

Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli, Elisabetta Paladino, Giuseppe A. Falci

quant-phSubmitted Dec 30, 2025• Announced Dec 31, 2025• 0 citations• Revised May 11, 2026 (v2)
v1 · Dec 30, 2025v2 · May 11, 2026
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Abstract

We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed.

Journal reference
CEUR Workshop Proceedings, Vol. 4153, Proceedings of the 3rd International Workshop on AI for Quantum and Quantum for AI (AIQxQIA 2025), 2026

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