Symplectic H2 Model Reduction for High-Dimensional Linear Quantum Systems

TL;DR

This paper introduces a symplectic model reduction method for high-dimensional linear quantum systems that preserves physical realizability and structure, enabling efficient and accurate reduced models.

Contribution

A novel symplectic Petrov-Galerkin framework and Quantum IRKA algorithm are developed for structure-preserving $ ext{H}_2$ model reduction of quantum systems.

Findings

Q-IRKA preserves symplecticity and physical realizability to machine precision.

Reduced models achieve high accuracy with moderate computational cost.

Reduction quality varies with dissipation, channel placement, and heterogeneity.

Abstract

The $\mathcal{H}_2$ model reduction problem for high-dimensional linear quantum systems is studied under the constraint of physical realizability (PR). This constraint requires preservation of the canonical commutation relations and the quantum input-output structure, and therefore prevents the direct use of standard projection methods. A symplectic Petrov-Galerkin framework is presented, in which reduced-order models automatically satisfy the PR identities by construction. Within this framework, a symplectic variant of the iterative rational Krylov algorithm is developed and referred to as Quantum IRKA (Q-IRKA). At each iteration, an enriched tangential rational Krylov pool is generated from shifted linear solves. A symplectic basis is then extracted by a Gram-Schmidt-type procedure, paired with symplectic conjugates, and normalized so that the reduced trial space satisfies the canonical symplectic constraint. The interpolation points are updated from selected mirror images of the poles of the current reduced-order model, while the reduced-order matrices are obtained exclusively by structure-preserving projection. Numerical experiments on low-channel oscillator-chain systems and on a bosonic Kitaev-chain-inspired benchmark show that Q-IRKA is effective for large-scale linear quantum systems. Symplecticity and PR are preserved to machine precision, and accurate reduced-order models are obtained with moderate computational cost. The results also show that reduction quality depends substantially on dissipation geometry, channel placement, heterogeneity, and reduced order. These findings indicate that scalable $\mathcal{H}_2$ model reduction of linear quantum systems can be achieved while strictly preserving the underlying physical structure.