Probabilistic Constrained Model Predictive Control for Schrodinger Equation with Finite Approximation

摘要

Recent technological progress has prompted significant interest in developing the control theory of quantum dynamics. Following the increasing interest in the control of quantum systems, this study deals with the control problem of the Schrodinger equation under stochastic perturbations. Model predictive control (MPC) is a kind of optimal feedback control, in which the control performance over a finite future is optimized. The objective of this study is to propose a design method of MPC for the Schrodinger equation with finite approximation under probabilistic constraints. For this purpose, the two-sided Chebyshev's inequality is applied to successfully handle probabilistic constraints with less computational load.