Quantum Entanglement Spin Encoding for Cognitive Modeling

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper proposes a novel approach to cognitive modeling utilizing quantum entanglement and spin encoding. The core idea is that quantum entanglement, with its inherent parallelism and non-classical correlations, can provide a more accurate representation of complex cognitive processes compared to traditional classical models. We outline a framework where neuronal states are represented by quantum spins in superposition and interference, controlled through entanglement manipulation. This allows for the simulation of cognitive phenomena, such as decision-making, memory, and attention, by modulating the entanglement and interference patterns between spins. The potential for capturing previously inaccessible cognitive behaviors through this quantum approach is explored, suggesting a fundamental shift in how we model the human mind. The model is presented as a conceptual architecture and relies on theoretical formulations rather than experimental validation.

DOI: 10.5281/zenodo.22222976

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