Metrology-assisted entanglement distribution in noisy quantum networks

TL;DR

This paper introduces a metrology-assisted approach to improve entanglement distribution in noisy quantum networks by using unsuccessful state conversions for noise estimation, enhancing the fidelity of target states without extra resource costs.

Contribution

It proposes a novel protocol that leverages failed conversion attempts for noise estimation, improving entanglement distribution efficiency in realistic, resource-constrained quantum networks.

Findings

Enhanced state conversion fidelity through embedded channel estimation.

Effective sequential, adaptive Bayesian estimation strategies.

Comparison showing advantages over distillation and quantum memory methods.

Abstract

We consider the distribution of high-dimensional entangled states to multiple parties via noisy channels and the subsequent probabilistic conversion of these states to desired target states using stochastic local operations and classical communication. We show that such state-conversion protocols can be enhanced by embedded channel-estimation routines at no additional cost in terms of the number of copies of the distributed states. The defining characteristic of our strategy is the use of those copies for which the conversion was unsuccessful for the estimation of the noise, thus allowing one to counteract its detrimental effect on the successfully converted copies. Although this idea generalizes to various more complex situations, we focus on the realistic scenario, where only finitely many copies are distributed and where the parties are not required to process multiple copies simultaneously. In particular, we investigate the performance of so-called one-successful-branch protocols, applied sequentially to single copies and an adaptive Bayesian estimation strategy. Finally, we compare our strategy to more general but less easily practically implementable strategies involving distillation and the use of quantum memories to process multiple copies simultaneously.