Dynamic Topological Quantum Neuron Networks

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces the concept of Dynamic Topological Quantum Neuron Networks (DTQNNs), a novel approach to computation leveraging the principles of quantum entanglement and neural networks. The core claim is that real-time adjustment of entanglement strength between neurons can enable more efficient and flexible computation compared to traditional neural networks. The underlying mechanism involves constructing a neural network using an entanglement network and dynamically adjusting the entanglement parameters – specifically, the coupling strength between qubits – to modulate neuronal connections and information transfer. This approach aims to overcome the information bottlenecks inherent in conventional neural networks by directly utilizing quantum entanglement for information processing. The theoretical framework presented explores the potential for DTQNNs to achieve enhanced computational capabilities and adaptive learning, representing a significant advancement in the intersection of quantum computing and artificial intelligence. The key mathematical concepts underpinning this approach are explored, including the description of entanglement entropy, quantum state manipulation, and network dynamics. We propose a model for DTQNNs based on the principles of topological quantum computation, focusing on the creation and control of non-local correlations between neurons. This model provides a foundation for future research and development in this emerging field.

DOI: 10.5281/zenodo.22272404

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