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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New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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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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Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.