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

• URL: http://arxiv.org/abs/2512.24393v1
• Date: Tue, 30 Dec 2025 18:13:48 GMT
• Title: Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
• Abstract summary: We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems.<n>The method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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