On the Bipartite Entanglement Capacity of Quantum Networks

TL;DR

This paper models the maximum entanglement distribution capacity in quantum networks with probabilistic entanglement swapping, proposing an optimization approach to compute the network's end-to-end entanglement capacity.

Contribution

It introduces a mixed-integer quadratically constrained program to exactly compute the bipartite entanglement capacity in arbitrary quantum network topologies under probabilistic conditions.

Findings

The MIQCP provides an exact upper bound on network capacity.

Application to real-world network topology demonstrates practical relevance.

Capacity computation accounts for probabilistic link presence and multiplexing effects.

Abstract

We consider the problem of multi-path entanglement distribution to a pair of nodes in a quantum network consisting of devices with non-deterministic entanglement swapping capabilities. Multi-path entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with pre-established link-level entanglement. Probabilistic entanglement swapping, on the other hand, limits the amount of entanglement that is shared between the nodes; this is especially the case when, due to architectural and other practical constraints, swaps must be performed in temporal proximity to each other. Limiting our focus to the case where only bipartite entangled states are generated across the network, we cast the problem as an instance of generalized flow maximization between two quantum end nodes wishing to communicate. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem for networks with arbitrary topology. We then compute the overall network capacity, defined as the maximum number of EPR states distributed to users per time unit, by solving the flow problem for all possible network states generated by probabilistic entangled link presence and absence, and subsequently by averaging over all network state capacities. The MIQCP can also be applied to networks with multiplexed links. While our approach for computing the overall network capacity has the undesirable property that the total number of states grows exponentially with link multiplexing capability, it nevertheless yields an exact solution that serves as an upper bound comparison basis for the throughput performance of easily-implementable yet non-optimal entanglement routing algorithms. We apply our capacity computation method to several networks, including a topology based on SURFnet -- a backbone network used for research purposes in the Netherlands.