SOFT COMPUTATION USING ARTIFICIAL NEURAL ESTIMATION AND LINEAR MATRIX INEQUALITY TRANSMUTATION FOR CONTROLLING SINGULARLY-PERTURBED CLOSED TIME-INDEPENDENT QUANTUM COMPUTATION SYSTEMS, PART A: BASICS AND APPROACH
This article presents basic background and approach methods that are needed for implementing a new method of intelligent control for time-independent quantum computing systems. This includes basic background on quantum computing, supervised neural networks, linear matrix inequalities, and model order reduction. The presented basics will be utilized in the second part of the paper for applying a new implementation of intelligent hierarchical control method within quantum computing where the obtained results are satisfying for the robust control of time-independent closed quantum computing systems.
linear matrix inequality transmutation quantum computing model order reduction identification parameter estimation recurrent supervised neural networks state feedback control transformation