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.

中文翻译：

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嘈杂量子网络中的计量辅助纠缠分布

我们考虑通过噪声通道将高维纠缠状态分布到多方，然后使用随机局部操作和经典通信将这些状态概率转换为所需的目标状态。我们表明，这种状态转换协议可以通过嵌入式通道估计例程来增强，而不会增加分布式状态的副本数量。我们策略的定义特征是使用那些转换不成功的副本来估计噪声，从而允许人们抵消其对成功转换的副本的不利影响。虽然这个想法可以推广到各种更复杂的情况，但我们关注的是现实场景，仅分发有限数量的副本，并且各方不需要同时处理多个副本。特别是，我们研究了所谓的单成功分支协议的性能，依次应用于单副本和自适应贝叶斯估计策略。最后，我们将我们的策略与更通用但不太容易实现的策略进行比较，这些策略涉及蒸馏和使用量子存储器同时处理多个副本。