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Optimal Quantum Filtering and Quantum Feedback Control
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Quantum mechanical systems exhibit an inherently probabilistic nature upon measurement. Using a quantum noise model to describe the stochastic evolution of the open quantum system and working in parallel with classical indeterministic control theory, we present the theory of nonlinear optimal quantum feedback control. The resulting quantum Bellman equation is then applied to the explicitly solvable quantum linear-quadratic-Gaussian (LQG) problem which emphasizes many similarities with the corresponding classical control problem.
Forward citations
Cited by 3 Pith papers
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Remote entanglement of massive oscillators via wire-mediated Coulomb interaction
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Optimal parametric driving in Gaussian quantum systems reduces impulse estimation variance by up to a factor of two relative to steady-state operation.
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Quantum Filtering and Stabilization of Dissipative Quantum Systems via Augmented Neural Ordinary Differential Equations
An augmented neural ODE framework learns single-qubit states and time-dependent dissipation parameters from simulated weak-measurement data, and supports PD and LQR feedback control.
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