Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
A machine learning-enhanced greybox framework was used to develop quantum control protocols for open qubits facing complex, non-Markovian noise. By combining a known physical model with a neural network trained on synthetic data, the method successfully captured environmental effects. It achieved high gate fidelities above 90% under various noise types like Random Telegraph and Ornstein-Uhlenbeck noise.