Quantum Enhanced Deep Reinforcement Learning for Distribution Network Reconfiguration

Abstract:Modern smart grids generate increasingly rich operational data and deploy greater numbers of remotely controlled switching assets than ever before, creating new opportunities for data-driven distribution network reconfiguration (DNR). DNR optimizes grid topology to improve efficiency, reduce power losses, and maintain operational constraints, but finding optimal switch configurations remains a computationally demanding combinatorial problem. In this paper, we propose a deep reinforcement learning (DRL) framework for static DNR to systematically investigate how the Q-network architecture affects both solution quality and learning performance. We evaluate multiple architectures within a controlled DRL framework, varying only the Q-network to isolate the contribution of each architectural choice. Our results show that quantum-enhanced Q-networks can achieve higher rewards with fewer parameters than their classical counterpart, suggesting that such architectures are a promising direction for combinatorial power systems optimization.

arXiv:2609.40327 [quant-ph]

(or arXiv:2609.40327v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.40327 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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