Soft Computation Using Artificial Neural Estimation And Linear Matrix Inequality Transmutation For Controlling Singularly-Perturbed Closed Timeindependent Quantum Computation Systems, Part B: Hierarchical Regulation Implementation

Abstract

Abstract A new method of intelligent control for time-independent closed quantum computation systems is introduced in this second part of the paper. The goal is to apply a new implementation of intelligent hierarchical control method within quantum computing systems where the obtained results are satisfying for the robust control of time-independent quantum computations. The new method utilizes supervised recurrent artificial neural networks (ANN) to estimate parameters of the [ A ]transformed system matrix After system matrix estimation is performed, linear matrix inequality (LMi) is used to detemvne the permutation matrix [P] so that a complete system transmutation {[ B ], [ C ], [ D ]} is accomplished. The transformed system model is then reduced using singular perturbation and state feedback control is implemented for system performance enhancement. In quantum computing and mechanics, a closed system is an isolated system that can’t exchange energy or matter with its surroundings and doesn’t interact ...