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arXiv:2205.01961 · Automatica

Model robustness for feedback stabilization of open quantum systems

Weichao Liang, Nina H. Amini

Published Feb 27, 2024• 13 citations
arXivHTMLPDF
DOI
10.1016/j.automatica.2024.111590

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We investigate the impact of undesired Markovian couplings to external systems on the stabilization towards pure states or subspaces of continuously monitored quantum systems under open-loop and feedback control protocols. In particular, we provide sufficient conditions on perturbations to maintain stability of the target and demonstrate the boundedness in mean of the solutions of perturbed systems under open-loop protocols. Effect on the stability of feedback protocols is also analyzed. This work is a step toward a comprehensive robustness analysis of measurement-based control of quantum systems with respect to model perturbations.
At the quantum level, feedback-loops have to take into account measurement back-action. We present here the structure of the Markovian models including such back-action and sketch two stabilization methods: measurement-based feedback where an open quantum system is stabilized by a classical controller; coherent or autonomous feedback where a quantum system is stabilized by a quantum controller with decoherence (reservoir engineering). We begin to explain these models and methods for the photon box experiments realized in the group of Serge Haroche (Nobel Prize 2012). We present then these models and methods for general open quantum systems.
This review develops a control-theoretic perspective on measurement-based feedback for continuously monitored open quantum systems, with the main analysis focused on finite-dimensional systems governed by diffusive stochastic master equations. We introduce the relevant state-space, invariant-subspace, and quantum non-demolition structures, interpret quantum filtering as nonlinear observer dynamics, and review open-loop asymptotics, filter stability, state-feedback stabilization, robustness, and reduced-order observer-based control. Particular emphasis is placed on a recurrence--contraction framework, in which Hamiltonian feedback removes non-target invariant obstructions while measurement-induced dynamics provide local exponential contraction. Although the detailed analysis is developed for finite-dimensional diffusive models, the underlying measurement--estimation--feedback architecture is relevant across a broad range of quantum platforms. We further discuss implementation challenges and open problems involving scalable estimation, sampling and delay, adaptation, hybrid and non-Markovian dynamics, practical stability, optimal control, and learning-based design. By organizing these developments around invariance, estimation, recurrence, and contraction, the review provides a tutorial bridge between measurement-based quantum feedback and nonlinear stochastic control.