Neuroinformational Flow of Neurons via Quantum Entropy Encoding

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel framework for understanding neuronal information processing, moving beyond the conventional emphasis on synaptic transmission. We hypothesize that neuronal information encoding relies fundamentally on the quantum entropy extracted from correlations between neurons, mediated by quantum entanglement. While synaptic transmission undoubtedly plays a role in facilitating information transfer, it is posited as a secondary mechanism supporting the primary quantum-based encoding process. We introduce a theoretical model leveraging the concept of quantum entanglement to describe how neuronal states are measured and how this measurement yields information that can be used to build a neuronal network's information processing model. The core idea is that the inherent quantum fluctuations within neurons, when coupled through entanglement, provide a source of information that can be quantified as entropy, forming the basis for neuronal communication. This approach challenges the prevailing view of neuronal information processing and opens new avenues for exploring the potential of quantum mechanics in biological systems. Mathematical formalism is presented to illustrate key aspects of this framework, focusing on entropy calculations and entanglement correlation analysis.

DOI: 10.5281/zenodo.22152681

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Publisher

Abstract

DOI: 10.5281/zenodo.22152681

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| \Psi(t) \rangle

represents the quantum state of the neuron at time *t*, and

\mathcal{H}

is the Hamiltonian operator describing the neuron's dynamics. We explore scenarios involving multiple inputs and their influence on the superposition state. The model demonstrates the potential for enhanced information processing compared to conventional models, particularly in scenarios requiring parallel computation and robust information storage. Furthermore, we discuss the implications of this approach for understanding and potentially mimicking biological neural computation. The model presented offers a theoretical foundation for exploring quantum computation within biological systems.