A greybox framework combining whitebox physics with a neural-network blackbox trained on synthetic data achieves over 90% gate fidelity for a qubit under non-Markovian noise.
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Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
A greybox framework combining whitebox physics with a neural-network blackbox trained on synthetic data achieves over 90% gate fidelity for a qubit under non-Markovian noise.