Scalable determination of multipartite entanglement in quantum networks

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED npj Quantum Information Pub Date : 2024-08-08 DOI:10.1038/s41534-024-00867-0
Wei-Ting Kao, Chien-Ying Huang, Tung-Ju Tsai, Shih-Hsuan Chen, Sheng-Yan Sun, Yu-Cheng Li, Teh-Lu Liao, Chih-Sung Chuu, He Lu, Che-Ming Li
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Abstract

Quantum networks comprised of entangled end nodes serve stronger than the classical correlation for unparalleled quantum internet applications. However, practical quantum networking is affected by noise, which at its worst, causes end nodes to be described by pre-existing classical data. In such untrusted networks, determining quantum network fidelity and genuine multi-node entanglement becomes crucial. Here, we show that determining quantum network fidelity and genuine N-node entanglement in an untrusted star network requires only N + 1 measurement settings. This method establishes a semi-trusted framework, allowing some nodes to relax their assumptions. Our network determination method is enabled by detecting genuine N-node Einstein-Podolsky-Rosen steerability. Experimentally, using spontaneous parametric down-conversion entanglement sources, we demonstrate the determinations of genuine 3-photon and 4-photon quantum networks and the false positives of the widely used entanglement witness, the fidelity criterion of 1/2. Our results provide a scalable method for the determination of multipartite entanglement in realistic quantum networks.

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量子网络中多方位纠缠的可扩展测定
由纠缠末端节点组成的量子网络比经典相关性更强,可用于无与伦比的量子互联网应用。然而,实际的量子网络会受到噪声的影响,在最糟糕的情况下,噪声会导致终端节点被先前存在的经典数据所描述。在这种不可信任的网络中,确定量子网络保真度和真正的多节点纠缠变得至关重要。在这里,我们展示了在不信任的星形网络中确定量子网络保真度和真正的 N 节点纠缠只需要 N + 1 个测量设置。这种方法建立了一个半信任框架,允许一些节点放松他们的假设。我们的网络确定方法是通过检测真正的 N 节点爱因斯坦-波多尔斯基-罗森转向性来实现的。实验中,我们利用自发参数下变频纠缠源,证明了真正的三光子和四光子量子网络的确定,以及广泛使用的纠缠见证--1/2 的保真度标准--的假阳性。我们的成果为确定现实量子网络中的多方纠缠提供了一种可扩展的方法。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
期刊最新文献
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