Optimal Dynamic Network Reconfiguration Using Hybrid Quantum Deep Q-Networks
Wenzhuo Shi, Jiaqi Ruan, Junyu Chen, Yibo Ding, Huayi Wu, Zhao Xu, Yuhua Du, Yigeng Huangfu · 2024
This paper introduces the application of a Hybrid Quantum Deep Q-Network (HQDQN) to solve dynamic network reconfiguration problem in distribution networks. Integrating quantum computing with deep reinforcement learning, the HQDQN is tested on the classical IEEE 33-node test feeder. It significantly outperforms traditional Deep Q-Network (DQN) models in energy efficiency. The results demonstrate the potential of quantum-enhanced machine learning algorithms to improve the operational efficiency of power grids.