IEEE International Conference on Quantum Computing and Engineering (QCE)

SDN-Based Entanglement Routing Strategy in Smart Power Grids Using Reinforcement Learning

Nicholas Skjellum, Mohamed Shaban, Muhammad Ismail

Published Aug 30, 2025• 0 citations

Abstract

Entanglement distribution in power grids can enable distributed quantum sensing and entanglement-based quantum key distribution, resulting in improved fault detection, real time diagnostics, provable security, etc. To distribute entanglements between remote endpoints, intermediate nodes also contribute by performing entanglement swapping to establish entangled states between these endpoints. In a power-grid network, existing routes may vary in performance over time due to unexpected traffic loads and changes in link quality. These conditions threaten the throughput of network-wide entanglement distribution. To maintain the throughput of entangled states, a quantum-enabled network needs to select ideal entanglement routes with high success rates for distribution while minimizing latency. To achieve this goal, we propose a software-defined networking (SDN) architecture that separates the data plane (responsible for entanglement distribution) from the control plane (responsible for route selection), enabling centralized monitoring and control of the quantum network. The control plane collects real-time metrics, including node resources (e.g., available qubits) and link conditions (e.g., noise levels and throughput rates). Leveraging this global network view, we implement a reinforcement learning (RL) agent within the SDN control plane to dynamically identify optimal entanglement distribution paths. Simulation results on the IEEE-14 bus power grid demonstrate that our RL-based routing strategy achieved a 100 % success rate in fulfilling entanglement distribution requests.

DOI: 10.1109/qce65121.2025.10308

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