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arXiv 2311.04583quant-ph
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非对称量子网络中的非定域关联

Nonlocal correlations in an asymmetric quantum network

Souradeep Sasmal, Shyam Sundar Mahato, Alok Kumar Pan

发表信息：
Phys. Rev. A 107, 022425 (2023)
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AI总结：

本文研究非对称双局域与三局域量子网络，推导新型非定域性不等式，并用平方和技术给出无维度假设的最优量子违背及白噪声鲁棒性。

摘要：

在多参与方、多源网络 Bell 实验中展现出的非定域性，在概念上不同于涉及单一共同源的标准多参与方 Bell 非定域性。本文通过引入非对称双局域以及三局域网络情形的若干变体，超越了两个边缘参与方拥有相同数量测量设置的典型双局域网络情形。我们首先引入一种非对称双局域网络，其中一个边缘参与方（例如 Alice）接收 $2^{n-1}$ 个输入，而另一个边缘参与方（例如 Charlie）接收 $n$ 个输入。我们推导了非对称双局域性不等式的两个变体，并证明了它们的最优量子违背。此外，我们研究了两类非对称三局域情形：(i) 两个边缘参与方各接收 $2^{n-1}$ 个输入，另一个边缘参与方接收 $n$ 个输入；(ii) 一个边缘参与方接收 $2^{n-1}$ 个输入，另外两个边缘参与方各有 $n$ 个输入。我们采用一种精巧的平方和技术，使我们能够在非对称双局域和三局域情形下均不假设系统维度，即可评估所提出网络不等式的量子最优值。此外，我们证明了所提出不等式的量子违背在白噪声存在时具有鲁棒性。

英文摘要：

The nonlocality revealed in a multiparty multisource network Bell experiment is conceptually different than the standard multiparty Bell nonlocality involving a single common source. Here, by introducing variants of asymmetric bilocal as well as trilocal network scenarios, we go beyond the typical bilocal network scenario where both the edge parties have an equal number of measurement settings. We first introduce an asymmetric bilocal network where one of the edge parties (say, Alice) receives $2^{n-1}$ inputs and the other edge party (say, Charlie) receives $n$ inputs. We derive two variants of asymmetric bilocality inequalities and demonstrate their optimal quantum violations. Further, we explore two types of asymmetric trilocal scenarios: (i) when two edge parties receive $2^{n-1}$ inputs each and the other edge party receives $n$ inputs, and (ii) when one edge party receives $2^{n-1}$ inputs, and the other two edge parties have $n$ inputs each. We use an elegant sum-of-squares technique that enables us to evaluate the quantum optimal values of the proposed network inequalities without assuming the dimension of the systems for both the asymmetric bilocal as well as the trilocal scenarios. Further, we demonstrate the robustness of the quantum violations of the proposed inequalities in the presence of white noise.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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