A new symplectic framework and Q-IRKA algorithm achieve H2-optimal model reduction for linear quantum systems while preserving physical realizability by construction.
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Machine learning trains an ensemble optimal control scheme to pick optimal measurement times for non-Markovian quantum noise parameters, reaching near Cramér-Rao bound precision.
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Symplectic H2 Model Reduction for High-Dimensional Linear Quantum Systems
A new symplectic framework and Q-IRKA algorithm achieve H2-optimal model reduction for linear quantum systems while preserving physical realizability by construction.
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Learning Non-Markovian Noise via Ensemble Optimal Control
Machine learning trains an ensemble optimal control scheme to pick optimal measurement times for non-Markovian quantum noise parameters, reaching near Cramér-Rao bound precision.