Effects of parametric uncertainties in cascaded open quantum harmonic oscillators and robust generation of Gaussian invariant states
For quantum stochastic networks, this work provides a method to robustly generate Gaussian states, though it is incremental as it extends existing perturbation analysis to cascaded systems.
This paper addresses robust generation of Gaussian invariant states in cascaded open quantum harmonic oscillators under parametric uncertainties. It develops infinitesimal perturbation analysis to minimize mean square sensitivity of purity via symplectic transformations, demonstrated with a numerical example.
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. We also discuss a connection of the sensitivity index with classical statistical distances and outline infinitesimal perturbation analysis for translation invariant cascades of identical oscillators. The findings of the paper are applicable to robust state generation in quantum stochastic networks.