Quantum Network Research Optimizes Entanglement Purification

A new preprint explores concurrent entanglement purification scheduling in dynamic quantum networks, a critical challenge for building reliable quantum communication. The research proposes a mixed-integer linear programming scheduler and develops reinforcement learning schedulers (DQN, PPO, SAC) to maximize scheduled requests while balancing success rates against Bell-pair consumption. This work aims to optimize the delivery of high-fidelity end-to-end entanglement amidst competing requests and heterogeneous links.