基于自关联的量子神经网络

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum neural networks (QNNs) leveraging the inherent properties of self-associative quantum systems. Traditional quantum neural networks often struggle with computational complexity and require extensive error correction, limiting their practical applicability. This research proposes a new QNN architecture that utilizes self-associative quantum states to dramatically reduce the number of parameters and enhance the network's ability to learn complex patterns. We demonstrate the superiority of this approach through theoretical analysis and preliminary experimental results on simplified quantum circuits, showcasing significant improvements in both training speed and model capacity compared to existing QNNs. The core mechanism involves dynamically adjusting the entanglement structure within the self-associative system to optimize information flow and propagation, leading to enhanced gradient propagation and reduced computational burden. Further, we explore the potential of this architecture for tackling specific machine learning tasks, including pattern recognition and classification.

DOI: 10.5281/zenodo.22621921

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