Title: Algorithmic Complexity of Cognitive Networks via Quantum Simulation

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Cognitive networks, mimicking the human brain, exhibit complex interactions and emergent behaviors. Traditional complexity measures, while useful, often fail to adequately represent the subtle quantum-influenced processes underpinning reasoning and learning. This paper investigates the algorithmic complexity of cognitive networks through a novel quantum simulation approach, aiming to quantify and potentially enhance the network's inherent complexity. We propose a quantum algorithm designed to explore the potential state space of the network, revealing underlying quantum-enhanced reasoning pathways. The research seeks to provide a deeper, more nuanced understanding of cognitive systems by utilizing the principles of quantum mechanics. This work addresses a critical gap in our understanding of how quantum phenomena might influence network dynamics.

DOI: 10.5281/zenodo.22251806

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