The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
This work addresses the challenge of modeling non-causal relationships in probabilistic graphical models for researchers in quantum computing and AI, but it appears incremental as it builds on existing quantum-like frameworks.
The paper tackles the problem of integrating acausal semantic similarities into quantum-like Bayesian networks, resulting in a model where events can be represented in vector spaces with quantum parameters derived from these similarities.
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.