基于量子自旋的自适应神经网络训练

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive neural network training framework leveraging the inherent quantum spin properties. Traditional neural network optimization methods often rely on static parameter adjustments, limiting the network's adaptability and potential for generalization. We propose a quantum spin neural network (QSN) architecture that utilizes quantum spin dynamics to dynamically adjust network weights and biases during training. This framework allows for inherent self-adaptation, enabling the network to learn optimal representations and improve performance across a wider range of datasets and tasks. The study demonstrates the effectiveness of this approach through extensive simulations and preliminary experiments on a benchmark dataset, showcasing significant improvements in generalization and convergence speed compared to conventional methods. The core mechanism involves utilizing quantum spin dynamics to iteratively refine the network's parameters, promoting a more robust and flexible learning process. This research presents a promising avenue for advancing neural network research by incorporating quantum phenomena into the training paradigm.

DOI: 10.5281/zenodo.22462062

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