Quantum Degenerate Information-Based Neural Computation

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper proposes a novel neural computation model leveraging the principles of quantum degenerate information (QDI). Traditional neuromorphic computing architectures often face limitations in learning speed and computational efficiency, particularly when dealing with complex tasks. Our approach utilizes the quantum mechanical phenomenon of QDI to create a fundamentally different neural network structure. We simulate the quantum-mechanical aspects of synaptic connections, representing synaptic weights as quantum state superpositions. Quantum entanglement and decoherence are harnessed to store and process information, and connections between neurons are mediated by quantum gates, facilitating parallel computation and information transfer. The theoretical framework presented demonstrates the potential for significantly enhanced learning and inference speeds compared to conventional approaches. The core claim of this work is that QDI-based neural computation offers a path toward surpassing the limitations of traditional neuromorphic computing. Mathematical notation is presented in plain text throughout this document. ---

DOI: 10.5281/zenodo.22244495

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