Titlebook: Linear Dynamical Quantum Systems; Analysis, Synthesis, Hendra I Nurdin,Naoki Yamamoto Book 2017 Springer International Publishing AG 2017 L

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Realization Theory for Linear Dynamical Quantum Systems,o be realized is decomposed into simpler subsystems, and how these subsystems can be realized, at least approximately, in the quantum optical setting is developed. In particular, it is shown that simpler realizations can be obtained for completely passive linear quantum systems.

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Linear Systems and Control Theory for Quantum Information,eling Gaussian fields, back-action evasion, perfect state transfer in a linear quantum network, and robust quantum amplification. The two experiments are demonstrations of enhancement of optical squeezing via static coherent feedback and generation of a spin-squeezed state in an atomic ensemble via measurement-based feedback control.

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Introduction,nd open quantum systems, the Markov property, and quantum Langevin equations. This chapter then proceeds with an introduction to linear quantum systems and several physical examples thereof, including optical cavities, non-degenerate and degenerate parametric amplifiers, and large atomic ensembles.

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Quantum Filtering for Linear Dynamical Quantum Systems,including a suitable notion of quantum conditional expectations. The chapter also briefly introduces robust observers for linear quantum systems to deal with modeling uncertainties for the purpose of estimation.

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Feedback Control of Linear Dynamical Quantum Systems,controlled Hudson–Parthasarathy QSDEs and controlled quantum filtering equations are introduced. Three control problems are formulated and solved: measurement-based linear quadratic Gaussian control, coherent linear quadratic Gaussian control, and coherent feedback . control. Examples are provided to illustrate each control method.

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Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Modelt common model for low-intensity imaging used in biomedical imaging. The algorithm being proposed is simple in tuning and has linear computation complexity with respect to the number of image elements so as to be able to process large data sets in a minimal time.