2014/08/08 by Onvaree Techakesari, Techakesari, Onvaree, Hendra I. Nurdin +1
Computer Science · Physics and Astronomy · #FOS: Electrical engineering #FOS: Physical sciences #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1408.1855
openalex publication_date 2014/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.