End-to-end entanglement of quantum network paths with multi-parameter states

TL;DR

This paper investigates how multi-parameter quantum states on network edges influence the end-to-end entanglement in quantum networks, revealing bounds, scaling behaviors, and implications for entanglement routing.

Contribution

It provides a theoretical analysis of end-to-end entanglement in quantum networks with multi-parameter states, highlighting bounds, distribution concentration, and routing implications.

Findings

End-to-end entanglement is bounded by a function of edge concurrence.

Distribution of entanglement concentrates around the mean as path length increases.

Optimal path selection becomes indeterminate with only entanglement guarantees.

Abstract

Long-range entanglement distribution in a quantum network relies on entanglement swapping at intermediate nodes along a network path to connect short-range entangled states established over the network edges. The end-to-end entanglement of a network path obtained via this process determines the utility of the network path for executing entanglement enabled tasks and for the design of entanglement routing protocols in the quantum network. Here, we study the end-to-end entanglement of paths in a quantum network when the edges are characterised by multi-parameter quantum states that may be considered to be the output of arbitrary and unknown quantum channels described by the network's edges. We find that over ensembles of multi-parameter states with fixed concurrence but varying density matrix elements, the end-to-end entanglement takes a range of values upper bounded by a function of the concurrence of the network-edge states. The scaling behaviour of the average end-to-end entanglement reveals that its distribution gets increasingly concentrated around the mean as the paths become longer. For a network path of a given length, the average end-to-end entanglement vanishes for edge concurrence values below a threshold that increases with the path-length. Whereas, for edge concurrence values greater than the threshold the average end-to-end entanglement increases faster with the length of the path. As an implication of our results, we show that the optimal path for entanglement distribution between a pair of end nodes, connected by alternate paths with multi-parameter states along the edges, can be indeterminate given only entanglement guarantees along the network edges.