论文标题
使用因果图扩展公平抽样假设
Extending the fair sampling assumption using causal diagrams
论文作者
论文摘要
在贝尔实验中丢弃不良的测量结果开启了检测漏洞，从而防止了非局部性的结论性证明。由于关闭检测漏洞代表了许多实际的铃铛实验的重大技术挑战，因此习惯上假设所谓的公平抽样假设（FSA）以其原始形式指出，集体上选择的统计数据是理想统计数据的公平样本。在这里，我们从因果推断的角度分析了FSA：我们得出了一种因果结构，该因果结构必须存在于任何因果模型中，忠实地封装了FSA。这提供了一种简单，直观和统一的方法，其中包括FSA的不同接受形式，并强调使用FSA时真正假设的内容。然后，我们证明FSA不仅可以在具有非理想探测器或传输损失的情况下应用，而且还可以在仅在相关性的部分中进行的理想实验中应用，例如，当粒子的目的地处于超匹配状态时。最后，我们证明FSA也适用于测试（真）多部分非局部性的多部分场景。
Discarding undesirable measurement results in Bell experiments opens the detection loophole that prevents a conclusive demonstration of nonlocality. As closing the detection loophole represents a major technical challenge for many practical Bell experiments, it is customary to assume the so-called fair sampling assumption (FSA) that, in its original form, states that the collectively postselected statistics are a fair sample of the ideal statistics. Here, we analyze the FSA from the viewpoint of causal inference: We derive a causal structure that must be present in any causal model that faithfully encapsulates the FSA. This provides an easy, intuitive, and unifying approach that includes different accepted forms of the FSA and underlines what is really assumed when using the FSA. We then show that the FSA can not only be applied in scenarios with non-ideal detectors or transmission losses, but also in ideal experiments where only parts of the correlations are postselected, e.g., when the particles' destinations are in a superposition state. Finally, we demonstrate that the FSA is also applicable in multipartite scenarios that test for (genuine) multipartite nonlocality.
