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Non-Shannon inequalities in the entropy vector approach to causal structures

机译:因果结构的熵向量法中的非香农不等式

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摘要

A causal structure is a relationship between observed variables that in general restricts the possible correlations between them. This relationship can be mediated by unobserved systems, modelled by random variables in the classical case or joint quantum systems in the quantum case. One way to differentiate between the correlations realisable by two different causal structures is to use entropy vectors, i.e., vectors whose components correspond to the entropies of each subset of the observed variables. To date, the starting point for deriving entropic constraints within causal structures are the so-called Shannon inequalities (positivity of entropy, conditional entropy and conditional mutual information). In the present work we investigate what happens when non-Shannon entropic inequalities are included as well. We show that in general these lead to tighter outer approximations of the set of realisable entropy vectors and hence enable a sharper distinction of different causal structures. Since non-Shannon inequalities can only be applied amongst classical variables, it might be expected that their use enables an entropic distinction between classical and quantum causal structures. However, this remains an open question. We also introduce techniques for deriving inner approximations to the allowed sets of entropy vectors for a given causal structure. These are useful for proving tightness of outer approximations or for finding interesting regions of entropy space. We illustrate these techniques in several scenarios, including the triangle causal structure.
机译:因果结构是观察变量之间的关系,通常会限制它们之间可能的相关性。这种关系可以由未观察到的系统介导,在经典情况下可以通过随机变量建模,在量子情况下可以通过联合量子系统建模。区分由两个不同的因果结构可实现的相关性的一种方法是使用熵向量,即其分量对应于所观察变量的每个子集的熵的向量。迄今为止,在因果结构内推导熵约束的起点是所谓的香农不等式(熵的正性,条件熵和条件互信息)。在目前的工作中,我们研究了当同时包含非香农熵不等式时会发生什么。我们表明,一般而言,这些导致可实现的熵向量集的更紧密的外部逼近,因此可以对不同因果结构进行更清晰的区分。由于非香农不等式只能在经典变量之间应用,因此可以预期,它们的使用可以实现经典因果结构和量子因果结构之间的熵区分。但是,这仍然是一个悬而未决的问题。我们还介绍了用于推导给定因果结构的熵向量的允许集合的内部近似的技术。这些对于证明外部逼近的紧密性或找到熵空间的有趣区域很有用。我们在几种情况下说明了这些技术,包括三角因果结构。

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