Recurrent Supervised Neural Computation and LMI Model Transformation for Order Reduction-Based Control of Linear Time-Independent Closed Quantum Computing Systems

摘要

This paper introduces a new method of intelligent control for closed quantum computation time-independent systems. The new method uses recurrent supervised neural network to identify certain parameters of the transformed system matrix [A{top}~]. Linear matrix inequality is then used to determine the permutation matrix [P] so that a complete system transformation {[B{top}~], [C{top}~], [D{top}~]} is achieved. The transformed model is then reduced using the method of singular perturbation and state feedback control is applied to enhance system performance. 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 with other quantum systems. In contrast to open quantum systems, closed quantum systems obey the unitary evolution and thus are information lossless (i.e., reversible). The experimental simulation results show that the new hierarchical control methodology simplifies the model of the quantum computing system and thus uses a simpler controller that produces the desired system response for performance enhancement.