基于量子自旋的神经网络的自适应学习

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing quantum spin systems to enhance the self-adaptive learning capabilities of neural networks. Traditional neural network architectures often rely on manually engineered parameters, limiting their flexibility and hindering their ability to generalize effectively across diverse datasets. We propose a novel approach that introduces quantum spin systems as a fundamental building block within the network architecture. The core mechanism involves utilizing the inherent self-adaptability of quantum spin states to dynamically adjust the network's parameters during training, leading to improved model robustness and generalization performance. This work explores the implementation of a quantum-inspired network architecture and examines its impact on key performance metrics, including accuracy and stability. The research underscores the potential of leveraging quantum mechanics to overcome limitations inherent in classical neural network training.

DOI: 10.5281/zenodo.22461831

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