Abstract
We introduce a repeater scheme to efficiently distribute multipartite entangled states in a quantum network with optimal scaling. The scheme allows to generate graph states such as 2D and 3D cluster states of growing size or GHZ states over arbitrary distances, with a constant overhead per node/channel that is independent of the distance. The approach is genuine multipartite, and is based on the measurement-based implementation of multipartite hashing, an entanglement purification protocol that operates on a large ensemble together with local merging/connection of elementary building blocks. We analyze the performance of the scheme in a setting where local or global storage is limited, and compare it to bipartite and hybrid approaches that are based on the distribution of entangled pairs. We find that the multipartite approach offers a storage advantage, which results in higher efficiency and better performance in certain parameter regimes. We generalize our approach to arbitrary network topologies and different target graph states.
Highlights
The distribution of entangled quantum states over large distances is a central task in quantum information processing
In23 a solution to this problem was proposed, where a quantum repeater scheme based on hashing–a deterministic entanglement purification protocol that operates on a large ensemble—is used to establish long-distance quantum communication between a sender and a receiver with overheads per channel that do not grow with the distance
We model noise via local depolarizing noise (LDN), which is given by the map and acts on a qubit with density matrix ρ in the following way:
Summary
We introduce a repeater scheme to efficiently distribute multipartite entangled states in a quantum network with optimal scaling. We discuss alternative schemes based on pairwise generation of entangled states, which are subsequently combined to form the desired multipartite target state (scheme B), and a hybrid approach that makes use of bipartite and multipartite elements (scheme C) We compare these three approaches, and develop optimized strategies to minimize the storage requirements of stations in the network.
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