A new symplectic framework and Q-IRKA algorithm achieve H2-optimal model reduction for linear quantum systems while preserving physical realizability by construction.
A survey of model reduction by balanced truncation and some new results,
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A dynamic subspace method parameterizes low-dimensional bases as geodesic paths on the Grassmannian to track evolving physics in nonlinear systems, achieving higher accuracy than static approximations at the same rank.
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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.