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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
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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.

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Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli, Elisabetta Paladino, Giuseppe A. Falci. 2025-12-30. Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise. https://arxiv.org/abs/2512.24393

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