Quantum Information in Biological Neural Networks: A Novel Approach to Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum information has emerged as a transformative technology with the potential to revolutionize various fields, and biological neural networks, with their intricate complexity and dynamic nature, represent a particularly promising area for exploration. This paper investigates the potential of quantum information to enhance learning mechanisms within biological neural networks, leveraging quantum entanglement and quantum computation to improve efficiency and model expressiveness. We propose a novel quantum neural network architecture that employs quantum entanglement to stabilize learning and optimize network parameters, addressing the limitations of conventional neural network learning. This research explores the fundamental principles behind such a system and highlights its potential for advancing our understanding of biological brain function. The core claim is that quantum information can significantly improve learning efficiency and model representation capabilities in biological neural networks.

DOI: 10.5281/zenodo.22185188

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