Integrating Quantum Computing into Optimal Control for Optimality and Stability in Microgrids

In this paper, we introduce a novel approach to address the computational challenges inherent in optimal control by leveraging quantum computing technology. We propose a hybrid quantum algorithm to theoretically address quadratic programming problems in Model Predictive Control (MPC) with inequality constraints. This approach aims to improve computational efficiency and provide innovative solutions to the challenges in power systems. Specifically, the theoretical method integrates the interior point method with the variational quantum algorithm, utilizing the parameter shift rule to solve quadratic programming problems with inequality constraints in MPC. Meanwhile, we also introduce how quantum computing can be employed to handle quadratic programming problems with various types of constraints, such as equality, inequality, or binary constraints. In addition, we achieve polynomial speedup in parameter design for MPC to ensure system stability by using quantum computing.