$\mathscr{H}_2$ Model Reduction for Linear Quantum Systems

TL;DR

This paper introduces an $$ norm-based method for reducing the complexity of linear quantum systems while ensuring physical realizability, using LMIs and a lifting variables approach for optimization.

Contribution

It develops a novel $$ norm-based model reduction technique that guarantees physical realizability through nonlinear constraints and extends to passive quantum systems.

Findings

The method effectively reduces model order while maintaining quantum system properties.

It outperforms existing criteria by providing an optimal reduced model.

Examples demonstrate the approach's efficacy for active and passive systems.

Abstract

In this paper, an $\mathscr{H}_2$ norm-based model reduction method for linear quantum systems is presented, which can obtain a physically realizable model with a reduced order for closely approximating the original system. The model reduction problem is described as an optimization problem, whose objective is taken as an $\mathscr{H}_2$ norm of the difference between the transfer function of the original system and that of the reduced one. Different from classical model reduction problems, physical realizability conditions for guaranteeing that the reduced-order system is also a quantum system should be taken as nonlinear constraints in the optimization. To solve the optimization problem with such nonlinear constraints, we employ a matrix inequality approach to transform nonlinear inequality constraints into readily solvable linear matrix inequalities (LMIs) and nonlinear equality constraints, so that the optimization problem can be solved by a lifting variables approach. We emphasize that different from existing work, which only introduces a criterion to evaluate the performance after model reduction, we guide our method to obtain an optimal reduced model with respect to the $\mathscr{H}_2$ norm. In addition, the above approach for model reduction is extended to passive linear quantum systems. Finally, examples of active and passive linear quantum systems validate the efficacy of the proposed method.