Speaker
Description
A quantum internet must distribute entanglement across imperfect repeaters, where the order in which each node waits, swaps, or purifies its links sets how fast and how reliably it is delivered. Choosing that order is a sequential decision problem: the dominant "swap-as-soon-as-possible'' (swap-asap) heuristic is optimal only for perfect hardware, and learned policies so far are tied to a single fixed network, leaving scalability open. We parameterise the per-node policy with a size-agnostic graph neural network (GraphSAGE) trained by Double-DQN, so one set of weights acts on a chain of any length. Trained on small imperfect chains ($N=4$-$10$), it transfers without retraining to larger ones ($N=10$-$15$) outperforming hand-crafted heuristics, with the advantage growing at the unseen sizes and vanishing only where swap-asap is already optimal.
| Topic | Machine learning techniques for experimental quantum information science |
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