Researchgreybox modelsquantum controlnon markovian noiseneural networks
Greybox Framework Enhances Quantum Optimal Control Fidelity
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Researchers apply a machine-learning-enhanced greybox framework to quantum optimal control for open quantum systems, combining a whitebox physical model with a neural-network blackbox trained on synthetic data. In tests, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. The paper discusses critical issues and limitations of the approach.
Scoring Rationale
Practical ML-backed quantum-control advance demonstrates actionable gains, but limited by preprint status and narrow, application-specific experimental validation.
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