最大化量子态分层模型的发散
Maximizing the divergence from a hierarchical model of quantum states
摘要：
我们研究多方相关性量化的Umegaki相对熵（分歧）从吉布斯家庭被称为一个层次模型。我们从最大熵原理推导出这些量，该原理在前面用来定义密切相关的不可约相关。我们指出量子态和概率向量之间的差异存在于分层模型中，在从分层模型的分歧和这种分歧的本地最大化。差异分别是缺少因子分解、不连续性和减少不确定性。我们讨论了可分量子比特态的互信息的全局最大化。
英文摘要：
We study many-party correlations quantified in terms of the Umegaki relative entropy (divergence) from a Gibbs family known as a hierarchical model. We derive these quantities from the maximum-entropy principle which was used earlier to define the closely related irreducible correlation. We point out differences between quantum states and probability vectors which exist in hierarchical models, in the divergence from a hierarchical model and in local maximizers of this divergence. The differences are, respectively, missing factorization, discontinuity and reduction of uncertainty. We discuss global maximizers of the mutual information of separable qubit states.
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