基于神经元模拟的通用量子算法证明

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel approach to demonstrating the feasibility of universal quantum algorithms using neural simulations. We propose a methodology for decomposing universal quantum algorithms into computational models amenable to neural simulation, leveraging the inherent parallelism of biological neural networks. This work aims to bridge the gap between theoretical quantum algorithms and the potential of neuromorphic computing, offering a new perspective on quantum algorithm design and potentially circumventing hardware limitations associated with traditional quantum computers. Specifically, we outline a framework where quantum circuits are mapped onto neural networks, enabling the exploration of quantum algorithms within a biologically inspired computational paradigm. The core of this approach lies in simplifying the complexity of quantum computation through the parallel processing capabilities of neural networks, ultimately providing a biological foundation for quantum algorithms. This research contributes to a deeper understanding of quantum computation and opens avenues for designing quantum algorithms with enhanced practicality and potentially reduced hardware requirements.

DOI: 10.5281/zenodo.22337814

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