Reinforcement Learning for Entanglement Distribution in Quantum Networks

What is it about?

We model a quantum network state as a graph and entanglement generation and swapping as edge operations in that graph. This allows a Reinforcement Learning based approach for finding an optimal entanglement distribution policy.

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Why is it important?

Quantum entanglement is the key resource in future quantum networks, which will enable quantum internet. For entanglement distribution, management of entanglement generation and swapping in the network is key, and hence, finding optimal ways to achieve distribution is of interest.