论文标题
量子liang信息流作为因果量词
Quantum Liang Information Flow as Causation Quantifier
论文作者
论文摘要
LIANG信息流是经典网络理论中广泛使用的数量，用于量化因果关系，并已广泛应用于资助和气候。这里最引人注目的方面是冻结/减去网络的某个节点，以确定其因果影响网络的其他节点。这种方法尚未应用于量子网络动力学。在这里，我们使用Von-Neumann熵将Liang信息流向量子域。我们建议使用它来评估网络各个节点的相对重要性，以影响目标节点。我们通过使用小量子网络来体现应用程序。
Liang information flow is a quantity widely used in classical network theory to quantify causation, and has been applied widely, for example, to finance and climate. The most striking aspect here is to freeze/subtract a certain node of the network to ascertain its causal influence to other nodes of the network. Such an approach is yet to be applied to quantum network dynamics. Here we generalize Liang information flow to the quantum domain using the von-Neumann entropy. Using that we propose to assess the relative importance of various nodes of a network to causally influence a target node. We exemplify the application by using small quantum networks.
