Control flow in active inference systems

TL;DR

This paper demonstrates that control flow in active inference systems can be represented as tensor networks, integrating quantum neural network frameworks to model biological systems across scales.

Contribution

It introduces a novel representation of control flow in active inference systems using tensor networks within quantum neural network frameworks.

Findings

Control flow systems can be represented as tensor networks.

Tensor networks can be implemented within quantum topological neural networks.

Implications for multi-scale biological system modeling are discussed.

Abstract

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.