论文标题
通过均匀性分布的传送性能提高
Gain in Performance of Teleportation with Uniformity-breaking Distributions
论文作者
论文摘要
可以利用有关输入状态的先前信息来提高量子传送的效率，我们使用忠诚度的前两个时刻对此进行了量化。输入知识是通过放松输入态分布中的均匀性假设并考虑不均匀分布的引入知识，即极性帽和von mises-fisher密度。对于这些分布，我们表明平均忠诚度增加了，而偏差随着有关输入合奏的信息内容的增加而减少，从而确立了其作为资源的作用。我们之间的两个分布之间的比较研究表明，对于有关输入的相同数量的信息内容，尽管两者的平均保真度收益率相同，但极性帽分布却“更好”，因为它提供了较小的偏差。此外，我们将先前信息的资源与协议中涉及的其他资源（如共享纠缠和经典交流）进行了对比。具体而言，我们观察到，与统一分布不同，完成任务所需的经典通信量随着可用于输入的信息增加而减少。我们还调查了先验信息在较高（三）维的传送中的作用，并报告了基于基于信息的传送的维度优势的签名。
Prior information about the input state can be utilized to enhance the efficiency of quantum teleportation which we quantify using the first two moments of fidelity. The input knowledge is introduced by relaxing the uniformity assumption in the distribution of the input state and considering non-uniform distributions, namely the polar cap and von Mises-Fisher densities. For these distributions,we show that the average fidelity increases while the deviation decreases with the increase of information content about the input ensemble thereby establishing its role as a resource. Our comparative study between these two distributions reveals that for the same amount of information content about inputs, although the average fidelity yield is the same for both, the polar cap distribution is "better" as it offers a smaller deviation. Moreover, we contrast the resource of prior information with other resources involved in the protocol like shared entanglement and classical communication. Specifically, we observe that unlike uniform distribution, the amount of classical communication required to fulfill the task decreases with the increase of information available for inputs. We also investigate the role of prior information in higher (three) dimensional teleportation and report the signatures of dimensional advantage in prior information-based teleportation.
