Influence of noise in entanglement-based quantum networks

TL;DR

This paper investigates how noise affects the quality of entanglement distribution in quantum networks, comparing different resource states and finding high-dimensional cluster states to be most robust in large-scale networks.

Contribution

It introduces an efficient method to evaluate fidelity in noisy quantum networks and identifies optimal resource states for large-scale entanglement distribution.

Findings

High-dimensional cluster states outperform other topologies in fidelity.

Noise impacts are quantitatively analyzed for various resource states.

Large networks benefit from high-dimensional entanglement structures.

Abstract

We consider entanglement-based quantum networks, where multipartite entangled resource states are distributed and stored among the nodes and locally manipulated upon request to establish the desired target configuration. Separating the generation process from the requests enables a pre-preparation of resources, hence a reduced network latency. It also allows for an optimization of the entanglement topology, which is independent of the underlying network geometry. We concentrate on establishing Bell pairs or tripartite GHZ states between arbitrary parties. We study the influence of noise in this process, where we consider imperfections in state preparation, memories, and measurements - all of which can be modeled by local depolarizing noise. We compare different resource states corresponding to linear chains, trees, or multi-dimensional rectangular clusters, as well as centralized topologies using bipartite or tripartite entangled states. We compute the fidelity of the target states using a recently established efficient method, the noisy stabilizer formalism, and identify the best resource states within these classes. This allows us to treat networks of large size containing millions of nodes. We find that in large networks, high-dimensional cluster states are favorable and lead to a significantly higher target state fidelity.