The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
Artificial Intelligence
2015-08-28 v1
Abstract
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.
Cite
@article{arxiv.1508.06973,
title = {The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks},
author = {Catarina Moreira and Andreas Wichert},
journal= {arXiv preprint arXiv:1508.06973},
year = {2015}
}
Comments
In proceedings of the 9th International Conference on Quantum Interactions, 2015