qRL: Reinforcement Learning Routing for Quantum Entanglement Networks

Quantum Internet aims to enable quantum communication between any two points, offering applications such as quantum key distribution (QKD), distributed quantum computing, and entanglement networks. However, the current quantum technology presents challenges with low entanglement (EPR pairs) generation rates, limited quantum memory capacity, and decoherence rates that often lead to unusable EPR pairs due to low fidelity. This presents a significant challenge for tasks such as routing. In this paper, we tackle this challenge by introducing qRL, a quantum-aware routing protocol that utilizes reinforcement learning to optimize quantum routing decisions. We show that qRL consistently outperforms traditional methods by maintaining higher fidelity routes and request success rates in different network configuration scenarios.