Tales told by coloured tangles

TL;DR

This paper introduces tangle machines, a topological diagrammatic formalism for modeling information flow in networks, with applications to Gaussian estimator fusion, quantum computations, and data learning.

Contribution

It presents the concept of tangle machines, explores their applications in covariance intersection, quantum computation, and learning from data, and discusses their potential in network analysis.

Findings

Tangle machines effectively model covariance intersection in Gaussian estimators.

They can represent adiabatic quantum computations.

The paper discusses methods for learning tangle machines from data.

Abstract

Tangle machines are a topologically inspired diagrammatic formalism to describe information flow in networks. This paper begins with an expository account of tangle machines motivated by the problem of describing `covariance intersection' fusion of Gaussian estimators in networks. It then gives two examples in which tangle machines tell stories of adiabatic quantum computations, and discusses learning tangle machines from data.