Quantum Entanglement-Based Simulation of Information Encoding and Decoding in Biological Systems

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This research investigates the potential of utilizing quantum entanglement to simulate information encoding and decoding mechanisms within biological neural networks. The core premise is that quantum phenomena, specifically entanglement, may play a more significant role in biological neuronal processing than previously acknowledged. This work proposes a novel approach employing quantum bits (qubits) to represent individual neurons, with connections between neurons established through quantum entanglement. The simulation aims to mimic the processes of synaptic transmission and signal propagation observed in biological neural networks. Mathematical formulations describe the evolution of the entangled qubit states under influence of simulated neuronal inputs, and the measurement processes are modeled to represent the observed output signals. This research offers a theoretical framework for exploring the interplay between quantum mechanics and biological neural systems, potentially leading to a deeper understanding of the fundamental principles governing brain function. The study utilizes a discrete-time simulation approach, allowing for a detailed analysis of the dynamics of the entangled neural network. Key performance indicators, such as the fidelity of information transfer and the robustness of the simulated network, are explored. The results suggest that quantum entanglement can provide a viable model for simulating complex neural processes, warranting further investigation into the potential for quantum computing to revolutionize our understanding of the brain.

DOI: 10.5281/zenodo.22136370

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