Reinforcement learning for flexible entanglement routing

We consider the problem of entanglement routing in heterogeneous quantum networks where each link and node may offer a different entanglement generation quality. We consider that each link offers a base rate and fidelity of entanglement generation. These base values can be altered by performing entanglement purification on each link. Furthermore, we consider that each repeater node offers a different probability of a successful entanglement swapping. With this setting, we design a reinforcement learning-based approach for flexible entanglement routing on future quantum networks. Specifically, we formulate the problem of entanglement routing in the Q-learning framework where the main objective of the agent is to deliver end-to-end entanglement while maximizing a user-specified objective function in the form of a weighted sum of achieve fidelity and rate. We demonstrate the efficacy and flexibility of developed framework by simulating random quantum networks where the random requests in fidelity maximizing, rate maximizing, or balanced mode are generated. We also numerically assess the effects of infrastructure developments and technology enhancements in future quantum networks.