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Quantum Computation Based on Neuronal Dynamics

Jincheng Zhang

Published Aug 29, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel quantum computing model that leverages the dynamics of neuronal behavior as its foundational building blocks. Unlike conventional quantum computing approaches reliant on purely classical qubits, this model utilizes quantum systems designed to mimic the dynamic changes observed in biological neural networks, specifically at the level of synaptic connections. The core principle involves employing quantum superposition and interference to construct quantum bits (qubits) representing the state of these simulated synapses. Algorithm execution is achieved through precise manipulation, or "quantum tuning," of these qubits. The proposed architecture represents a biologically-inspired paradigm shift in quantum computation, potentially offering advantages in terms of computational efficiency and resilience, particularly for problems exhibiting inherent biological complexity. This work outlines the theoretical framework, key equations, and the fundamental principles driving this new approach to quantum information processing.

DOI
10.5281/zenodo.22156526

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This paper proposes a novel neuron model leveraging the principles of quantum state superposition. Traditional neuron models, based on classical probability, struggle to accurately represent the complex information processing within biological neurons. This model introduces a fundamentally different approach by modeling synaptic connections as quantum states in superposition. The propagation of neural signals is then governed by the quantum mechanical interaction of these superimposed states. We define a mathematical framework incorporating key concepts from quantum mechanics, including superposition, entanglement, and quantum measurement, to describe the dynamics of this model. The core of the model lies in the equation: $ \frac{d| \Psi(t) \rangle}{dt} = \mathcal{H} | \Psi(t) \rangle $ where ∣Ψ(t)⟩| \Psi(t) \rangle∣Ψ(t)⟩ represents the quantum state of the neuron at time *t*, and H\mathcal{H}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.
This paper explores a novel approach to quantum algorithm design by leveraging the principles of biological synaptic plasticity. The core idea is to mimic the dynamic adjustments observed in neuronal synapses to dynamically optimize and adapt quantum algorithms. We propose a framework where the inherent plasticity of biological systems is translated into a mechanism for parameter tuning and error correction within quantum computations. Specifically, we model synaptic plasticity, such as Hebbian learning and spike-timing-dependent plasticity (STDP), and integrate these models into the optimization process of quantum algorithms. This approach aims to endow quantum algorithms with self-learning and adaptive capabilities, potentially leading to significant improvements in efficiency and robustness. The theoretical framework presented here provides a foundation for developing more resilient and adaptable quantum computing solutions, drawing inspiration from the remarkable adaptability of the human brain. We analyze the potential impact of this dynamic adaptation on various quantum algorithms and outline key challenges and future research directions. ---
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