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arXiv 2210.07263quant-ph
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不假设选择自由的因果网络中的实验非经典性

Experimental nonclassicality in a causal network without assuming freedom of choice

Emanuele Polino, Davide Poderini, Giovanni Rodari, Iris Agresti, Alessia Suprano, Gonzalo Carvacho, Elie Wolfe, Askery Canabarro, George Moreno, Giorgio Milani,… 展开作者

Emanuele Polino, Davide Poderini, Giovanni Rodari, Iris Agresti, Alessia Suprano, Gonzalo Carvacho, Elie Wolfe, Askery Canabarro, George Moreno, Giorgio Milani, Robert W. Spekkens, Rafael Chaves, Fabio Sciarrino

摘要:

在贝尔实验中,人们很自然地会寻求对相关性的因果解释,其中只有一个共同的原因对结果起作用。对于这种因果结构,贝尔不等式违反可以解释,只有当因果依赖建模为内在量子。在贝尔之外,还有一个巨大的因果结构景观可以见证非经典性,在某些情况下甚至不需要免费的外部输入。在这里,我们进行了一个光子实验,实现了这样一个例子:三角因果网络,由三个测量站成对连接的共同原因,没有外部输入。为了证明数据的非经典性,我们调整和改进了三种已知的技术:(i)基于机器学习的启发式测试,(ii)生成多项式Bell型不等式的数据种子膨胀技术和(iii)熵不等式。所展示的实验和数据分析工具具有广泛的适用性,为未来日益复杂的网络铺平了道路。

英文摘要:

In a Bell experiment, it is natural to seek a causal account of correlations wherein only a common cause acts on the outcomes. For this causal structure, Bell inequality violations can be explained only if causal dependencies are modelled as intrinsically quantum. There also exists a vast landscape of causal structures beyond Bell that can witness nonclassicality, in some cases without even requiring free external inputs. Here, we undertake a photonic experiment realizing one such example: the triangle causal network, consisting of three measurement stations pairwise connected by common causes and no external inputs. To demonstrate the nonclassicality of the data, we adapt and improve three known techniques: (i) a machine-learning-based heuristic test, (ii) a data-seeded inflation technique generating polynomial Bell-type inequalities and (iii) entropic inequalities. The demonstrated experimental and data analysis tools are broadly applicable paving the way for future networks of growing complexity.

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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