Optimal Fidelity-Aware Entanglement Distribution in Linear Quantum Networks

TL;DR

This paper addresses optimal strategies for entanglement distribution in linear quantum networks, proposing algorithms that coordinate purification and swapping to maximize end-to-end fidelity.

Contribution

It introduces a novel graph-theoretic framework for entanglement management and proposes two polynomial algorithms for optimizing purification and swapping decisions.

Findings

PtS outperforms StP in numerical simulations.

Omitting purification in StP yields significant benefits.

Graph matching models effectively represent entanglement operations.

Abstract

We study the problem of entanglement distribution in terms of maximizing a utility function that captures the total fidelity of end-to-end entanglements in a two-link linear quantum network with a source, a repeater, and a destination. The nodes have several quantum memories, and the problem is how to coordinate entanglement purification in each of the links, and entanglement swapping across links, so as to achieve the goal above. We show that entanglement swapping (i.e, deciding on the pair of qubits from each link to perform swapping on) is equivalent to finding a max-weight matching on a bipartite graph. Further, entanglement purification (i.e, deciding which pairs of qubits in a link will undergo purification) is equivalent to finding a max-weight matching on a non-bipartite graph. We propose two polynomial algorithms, the Purify-then-Swap (PtS) and the Swap-then-Purify (StP) ones, where the decisions about purification and swapping are taken with different order. Numerical results show that PtS performs better than StP, and also that the omission of purification in StP gives substantial benefits.