Intelligent Control of Reduced-Order Closed Quantum Computation Systems Using Neural Estimation and LMI Transformation

A new method of intelligent control for closed quantum computation time-independent systems is introduced. The introduced method uses recurrent supervised neural computing to identify certain parameters of the transformed system matrix \( [\tilde{\mathbf{A}}] \). Linear matrix inequality (LMI) is then used to determine the permutation matrix [P] so that a complete system transformation \(\{[\tilde{\mathbf{B}}], [\tilde{\mathbf{C}}], [\tilde{\mathbf{D}}]\}\) is achieved. The transformed model is then reduced using singular perturbation and state feedback control is implemented to enhance system performance. In quantum computation and mechanics, a closed system is an isolated system that can’t exchange energy or matter with its environment and doesn’t interact with other quantum systems. In contrast to an open quantum system, a closed quantum system obeys the unitary evolution and thus is information lossless that implies state reversibility. The experimental simulations show that the new hierarchical control simplifies the model of the quantum computing system and thus uses a simpler controller that produces the desired performance enhancement and system response.