基于量子态的自适应神经元网络架构

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, termed "Quantum-Aware Network" (QAN), that leverages the unique properties of quantum states to enhance learning and reasoning capabilities. The QAN architecture utilizes quantum entanglement and superposition to facilitate efficient parameter learning and improve the network's overall performance. We explore the potential advantages of this approach, particularly in scenarios demanding complex data analysis and rapid adaptation. The paper details the design of a quantum state network and discusses the theoretical foundation underpinning its potential benefits. Further, we present experimental results demonstrating the QAN's enhanced learning and inference capabilities compared to conventional neural networks, validating the core claims of improved parameter learning and enhanced network capacity.

DOI: 10.5281/zenodo.22632190

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