Optimized Quantum Networks

Optimized Quantum Networks
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优化的量子网络

DOI:
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发表时间:
2021
期刊:
影响因子:
6.4
通讯作者:
W. Dür
W. Dür
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
J. Miguel;A. Pirker;W. Dür

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经典网络的拓扑结构由节点之间的物理链路决定,在网络请求之后,链路用于建立所需的连接。量子网络提供了在网络请求之前生成不同类型纠缠的可能性,这些纠缠可以替代链路,并允许一个人以相同的资源状态满足多个网络请求。我们利用这一点来设计基于纠缠的量子网络,以适应其所需的功能,独立于底层的物理结构。选择要存储的纠缠的种类以满足所有期望的网络请求(即,从某个有限集合中选择的特定节点之间的并行二分或多分通信),但是以这样的方式,存储要求被最小化。这可以通过使用网络节点之间共享的多体纠缠态来实现,这些纠缠态可以通过本地操作转换为不同的目标状态。我们引入了一种集群算法来识别网络中的连接集群,以实现给定的所需功能,即基于纠缠的网络所需的网络拓扑,以及一种合并算法,该算法构建多部分纠缠资源状态,减少内存需求,以满足所有需求所需的网络请求。这导致所需时间和资源的显着减少,并提供了一个强大的工具来设计量子网络,这是基于纠缠的网络所独有的。
The topology of classical networks is determined by physical links between nodes, and after a network request the links are used to establish the desired connections. Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests, which can substitute links and allow one to fulfill multiple network requests with the same resource state. We utilize this to design entanglement-based quantum networks tailored to their desired functionality, independent of the underlying physical structure. The kind of entanglement to be stored is chosen to fulfill all desired network requests (i.e. parallel bipartite or multipartite communications between specific nodes chosen from some finite set), but in such a way that the storage requirement is minimized. This can be accomplished by using multipartite entangled states shared between network nodes that can be transformed by local operations to different target states. We introduce a clustering algorithm to identify connected clusters in the network for a given desired functionality, i.e. the required network topology of the entanglement-based network, and a merging algorithm that constructs multipartite entangled resource states with reduced memory requirement to fulfill all desired network requests. This leads to a significant reduction in required time and resources, and provides a powerful tool to design quantum networks that is unique to entanglement-based networks.
DOI: 10.1038/s41534-019-0139-x
发表时间: 2017-08
影响因子: 7.6
作者:
M. Pant;H. Krovi;D. Towsley;L. Tassiulas;Liang Jiang;P. Basu;D. Englund;S. Guha
通讯作者: M. Pant;H. Krovi;D. Towsley;L. Tassiulas;Liang Jiang;P. Basu;D. Englund;S. Guha