基于量子态的神经网络的自适应学习策略

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel neural network architecture leveraging quantum state dynamics for adaptive learning. Traditional neural networks often rely on static learning rates, limiting their ability to optimize for complex data patterns. We propose a quantum-inspired approach that utilizes the inherent properties of quantum states – specifically, self-avoidance and entanglement – to dynamically adjust learning parameters, resulting in enhanced prediction accuracy. This research explores the potential of harnessing quantum phenomena to improve the learning process, moving beyond the limitations of conventional methods. The core of this work is the development of a quantum state optimization algorithm that dynamically adjusts the network's parameters based on the quantum state's evolution. We analyze the impact of this dynamic adjustment on prediction accuracy and demonstrate a significant improvement over baseline techniques. The presented method offers a promising avenue for advancing neural network learning, particularly in scenarios demanding high precision and adaptability.

DOI: 10.5281/zenodo.22509848

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