RL-trained policies can schedule quantum network resources far more efficiently than hand-crafted heuristics, and LLMs can extract interpretable rules from these policies—useful as quantum networks scale beyond what's computationally trainable.
This paper uses reinforcement learning to schedule entanglement resources in quantum networks, enabling multiple quantum tasks to run simultaneously with minimal resources.