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 both Markovian and non-Markovian noise effects and is applied under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed.
Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
Riccardo Cantone;Shreyasi Mukherjee;Luigi Giannelli;Elisabetta Paladino;Giuseppe Falci
2026-01-01
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 both Markovian and non-Markovian noise effects and is applied under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed.File in questo prodotto:
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