TY - GEN
T1 - Integrating Quantum Computing into Optimal Control for Optimality and Stability in Microgrids
AU - Jing, Hang
AU - Li, Yan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85207396504&partnerID=8YFLogxK
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U2 - 10.1109/PESGM51994.2024.10689028
DO - 10.1109/PESGM51994.2024.10689028
M3 - Conference contribution
AN - SCOPUS:85207396504
T3 - IEEE Power and Energy Society General Meeting
BT - 2024 IEEE Power and Energy Society General Meeting, PESGM 2024
PB - IEEE Computer Society
T2 - 2024 IEEE Power and Energy Society General Meeting, PESGM 2024
Y2 - 21 July 2024 through 25 July 2024
ER -