基于量子拓扑的自适应神经网络

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture, termed "Quantum-Adaptive Neural Network" (QANN), predicated on the principles of quantum topology. We propose a dynamic adjustment mechanism leveraging quantum entanglement to facilitate rapid learning and optimization within complex systems. The QANN leverages the inherent properties of quantum entanglement to establish a global, instantaneous connection between network nodes, enabling a significantly enhanced learning capacity compared to traditional neural networks. We detail the design of the QANN, outlining the quantum entanglement topology used, the adjustment algorithm implemented, and the potential applications in areas requiring rapid adaptation and optimization. The core claim posits that the QANN's quantum entanglement-driven dynamic adjustment drastically improves learning speed and scalability across complex scenarios.

DOI: 10.5281/zenodo.22220230

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