scholarly journals Bulk private curves require large conditional mutual information

2021 ◽  
Vol 2021 (9) ◽  
Author(s):  
Alex May

Abstract We prove a theorem showing that the existence of “private” curves in the bulk of AdS implies two regions of the dual CFT share strong correlations. A private curve is a causal curve which avoids the entanglement wedge of a specified boundary region $$ \mathcal{U} $$ U . The implied correlation is measured by the conditional mutual information $$ I\left({\mathcal{V}}_1:\left.{\mathcal{V}}_2\right|\mathcal{U}\right) $$ I V 1 : V 2 U , which is O(1/GN) when a private causal curve exists. The regions $$ {\mathcal{V}}_1 $$ V 1 and $$ {\mathcal{V}}_2 $$ V 2 are specified by the endpoints of the causal curve and the placement of the region $$ \mathcal{U} $$ U . This gives a causal perspective on the conditional mutual information in AdS/CFT, analogous to the causal perspective on the mutual information given by earlier work on the connected wedge theorem. We give an information theoretic argument for our theorem, along with a bulk geometric proof. In the geometric perspective, the theorem follows from the maximin formula and entanglement wedge nesting. In the information theoretic approach, the theorem follows from resource requirements for sending private messages over a public quantum channel.

2013 ◽  
Vol 427-429 ◽  
pp. 1537-1543 ◽  
Author(s):  
Ya Fen Wang ◽  
Feng Zhen Zhang ◽  
Shan Jian Liu ◽  
Meng Huang

In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the querying image should convey high information about those similar to it. The method first generates a set of statistically representative visual patterns and uses the distributions of these patterns as images content descriptors. To measure the similarity of two images, we develop a method to compute the mutual information between their content descriptors. Two images with larger descriptor mutual information are regarded as more similar. We present experimental results, which demonstrate that mutual information is a more effective image similarity measure than those have been used in the literature such as Kullback-Leibler divergence and L2 norms.


2017 ◽  
Vol 19 (28) ◽  
pp. 18635-18645 ◽  
Author(s):  
Donghai Yu ◽  
Chunying Rong ◽  
Tian Lu ◽  
Pratim K. Chattaraj ◽  
Frank De Proft ◽  
...  

Strong correlations among aromaticity descriptors and information-theoretic quantities are unveiled, providing novel insights about aromaticity and antiaromaticity from different perspectives.


Author(s):  
R. V. Prasad ◽  
R. Muralishankar ◽  
S. Vijay ◽  
H. N. Shankar ◽  
Przemyslaw Pawelczak ◽  
...  

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