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Proceedings of the ... American Control Conference/Proceedings of the American Control Conference

An error bound on balanced truncation of quasi-balanceable linear quantum stochastic systems

Hendra I. Nurdin

Published Jun 1, 2014• 0 citations
Publisher

Abstract

Recently, an adaptation of the balanced truncation method for model reduction of classical (non-quantum) linear systems has been proposed for the class of linear quantum stochastic systems that are commonly employed as quantum stochastic Markov models of open quantum oscillators in quantum optics and related fields. In order to preserve the canonical commutation relations of the internal quantum harmonic oscillators, the similarity transformation that can be applied to linear quantum stochastic systems must be sympletic. Due to this restriction, general linear quantum stochastic systems need not have a balanced realization, in contrast to the classical case. However, a necessary and sufficient condition can be derived under which a linear quantum stochastic system can be identified as being quasi-balanceable, meaning that it can be transformed under a symplectic similarity transformation into a realization in which the controllability and observability Gramians are both diagonal but not the same diagonal matrix. In this paper, an error bound is derived for model reduction of quasi-balanceable linear quantum stochastic systems. It is shown that analogous to the classical case, the bound is determined by the Hankel singular values of the linear quantum stochastic system, and a Hankel singular value of the system is in fact the geometric mean of a corresponding pair of symplectic eigenvalues of the controllability and observability Gramians.

DOI
10.1109/acc.2014.6858919

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Recently, an adaptation of the balanced truncation method for model reduction of classical (non-quantum) linear systems has been proposed for the class of linear quantum stochastic systems that are commonly employed as quantum stochastic Markov models of open quantum oscillators in quantum optics and related fields. In order to preserve the canonical commutation relations of the internal quantum harmonic oscillators, the similarity transformation that can be applied to linear quantum stochastic systems must be sympletic. Due to this restriction, general linear quantum stochastic systems need not have a balanced realization, in contrast to the classical case. However, a necessary and sufficient condition can be derived under which a linear quantum stochastic system can be identified as being quasi-balanceable, meaning that it can be transformed under a symplectic similarity transformation into a realization in which the controllability and observability Gramians are both diagonal but not the same diagonal matrix. In this paper, an error bound is derived for model reduction of quasi-balanceable linear quantum stochastic systems. It is shown that analogous to the classical case, the bound is determined by the Hankel singular values of the linear quantum stochastic system, and a Hankel singular value of the system is in fact the geometric mean of a corresponding pair of symplectic eigenvalues of the controllability and observability Gramians.
This paper derives conditions under which co-diagonalization of the controllability and observability Gramians can be performed in a physically consistent manner for the class of linear quantum stochastic systems that are common in the fields of quantum optics and related areas like quantum nano- and optomechanical systems. Such a class is essentially a quantum analogue of classical (non-quantum) linear stochastic systems. However, unlike their classical counterparts, the laws of quantum mechanics impose nonlinear equality constraints on the system matrices of linear quantum stochastic systems, the so-called physical realizability constraints. Therefore, a meaningful model reduction procedure must yield a physically realizable reduced model satisfying these constraints. Previous work has shown that model reduction by subsystem truncation preserves physical realizability, but did not propose a method for truncating a subsystem. One candidate approach for this truncation is a quantum adaptation of the well-known balanced truncation method for classical linear time-invariant systems. It is shown in this paper that balanced realization for linear quantum stochastic systems is only possible under a strongly restrictive necessary and sufficient condition. However, the paper also derives less restrictive necessary and sufficient conditions for other realizations with simultaneously diagonal controllability and observability Gramians, and introduces the notion of a quasi-balanced realization. An example of the application of the results is provided to demonstrate quasi-balanced truncation in the linear quantum stochastic setting.
The purpose of this paper is to develop a model reduction theory for linear quantum stochastic systems that are commonly encountered in quantum optics and related fields, modeling devices such as optical cavities and optical parametric amplifiers, as well as quantum networks composed of such devices. It is shown that subsystem truncation preserves the physical realizability property of linear quantum stochastic systems, and that the property of complete passivity is invariant under subsystem truncation. However, generic linear quantum stochastic systems need not have a balanced realization under symplectic transformations. Therefore, alternative notions of balancing, including so-called quasi-balancing, are developed, and necessary and sufficient conditions are derived. A truncation error bound is derived for quasi-balanceable linear quantum stochastic systems and it is shown that all asymptotically stable completely passive linear quantum stochastic systems have a quasi-balanced realization. An example is provided to illustrate model reduction in the context of low-pass optical filtering of coherent light using a network of optical cavities.
This paper concerns the recently proposed quasi-balanced truncation model reduction method for linear quantum stochastic systems. It has previously been shown that the quasi-balanceable class of systems (i.e. systems that can be truncated via the quasi-balanced method) includes the class of completely passive systems. In this work, we refine the previously established characterization of quasi-balanceable systems and show that the class of quasi-balanceable systems is strictly larger than the class of completely passive systems. In particular, we derive a novel characterization of completely passive linear quantum stochastic systems solely in terms of the controllability Gramian of such systems. Exploiting this result, we prove that all linear quantum stochastic systems with a pure Gaussian steady-state (active systems included) are all quasi-balanceable, and establish a new complete parameterization for this important class of systems. Examples are provided to illustrate our results.
To make sense of the world around us, we develop models, constructed to enable us to replicate, describe, and explain the behaviours we see. Focusing on the broad case of sequences of correlated random variables, i.e., classical stochastic processes, we tackle the question of determining whether or not two different models produce the same observable behavior. This is the problem of identifiability. Curiously, the physics of the model need not correspond to the physics of the observations; recent work has shown that it is even advantageous -- in terms of memory and thermal efficiency -- to employ quantum models to generate classical stochastic processes. We resolve the identifiability problem in this regime, providing a means to compare any two models of a classical process, be the models classical, quantum, or `post-quantum', by mapping them to a canonical `generalized' hidden Markov model. Further, this enables us to place (sometimes tight) bounds on the minimal dimension required of a quantum model to generate a given classical stochastic process.
This paper is concerned with the generation of Gaussian invariant states in cascades of open quantum harmonic oscillators governed by linear quantum stochastic differential equations. We carry out infinitesimal perturbation analysis of the covariance matrix for the invariant Gaussian state of such a system and the related purity functional subject to inaccuracies in the energy and coupling matrices of the subsystems. This leads to the problem of balancing the state-space realizations of the component oscillators through symplectic similarity transformations in order to minimize the mean square sensitivity of the purity functional to small random perturbations of the parameters. This results in a quadratic optimization problem with an effective solution in the case of cascaded one-mode oscillators, which is demonstrated by a numerical example. The findings of the paper are applicable to robust state generation in quantum stochastic networks.