$\mathscr{H}_2$ Model Reduction for Augmented Model of Linear Non-Markovian Quantum Systems

TL;DR

This paper introduces an $ $ model reduction technique for linear non-Markovian quantum systems, reducing computational complexity while maintaining physical realizability, validated through a numerical example.

Contribution

It develops an $ $ model reduction method with necessary conditions and semidefinite programming for non-Markovian quantum systems, addressing physical realizability constraints.

Findings

The method effectively reduces model dimension.

Semidefinite programming solves the reduction problem.

Numerical example confirms the approach's validity.

Abstract

An augmented system model provides an effective way to model non-Markovian quantum systems, which is useful in filtering and control for this class of systems. However, since a large number of ancillary quantum oscillators representing internal modes of a non-Markovian environment directly interact with the principal system in these models, the dimension of the augmented system may be very large causing significant computational burden in designing filters and controllers. In this context, this paper proposes an $\mathscr{H}_2$ model reduction method for the augmented model of linear non-Markovian quantum systems. We first establish necessary and sufficient conditions for the physical realizability of the augmented model of linear non-Markovian quantum systems, which are more stringent than those for Markovian quantum systems. However, these physical realizability conditions of augmented system model pose non-convex constrains in the optimization problem of model reduction, which makes the problem different from the corresponding classical model reduction problem. To solve the problem, we derive necessary conditions for determining the input matrix in the reduced model, with which a theorem for designing the system matrix of the ancillary system in the reduced system is proved. Building on this, we convert the nonlinear equality constraints into inequality constraints so that a semidefinite programming algorithm can be developed to solve the optimization problem for model reduction. A numerical example of a two-mode linear quantum system driven by three internal modes of a non-Markovian environment validates the effectiveness of our method.