Quantifying Genuine Multipartite Correlations and their Pattern Complexity
Quantifying Genuine Multipartite Correlations and their Pattern Complexity
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DOI：
10.1103/physrevlett.119.140505
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发表时间：
2017-10-06
影响因子：
8.6
通讯作者：
Susa, Cristian E.
中科院分区：
文献类型：
--
作者：
Girolami, Davide;Tufarelli, Tommaso;Susa, Cristian E.
我们提出了一个信息论框架来量化经典和量子系统中的多部分相关性，回答诸如在十个粒子的给定状态下七部分相关性的数量是多少之类的问题？我们确定了真正的多方相关性的度量，即不能归因于二方相关性的统计依赖性，满足一组所需的属性。受复杂性科学中发展的思想的启发，我们引入了编织的概念来对显示不同相关模式但无法通过相关性度量来区分的状态进行分类。状态的编织被定义为每个阶相关性的加权和。编织测度是多部分系统中相关结构复杂性的良好描述。
We propose an information-theoretic framework to quantify multipartite correlations in classical and quantum systems, answering questions such as what is the amount of seven-partite correlations in a given state of ten particles? We identify measures of genuine multipartite correlations, i.e., statistical dependencies that cannot be ascribed to bipartite correlations, satisfying a set of desirable properties. Inspired by ideas developed in complexity science, we then introduce the concept of weaving to classify states that display different correlation patterns, but cannot be distinguished by correlation measures. The weaving of a state is defined as the weighted sum of correlations of every order. Weaving measures are good descriptors of the complexity of correlation structures in multipartite systems.