Adaptive Neuron Connection for Quantum Neural Networks

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum neural networks (QNNs) hold immense promise for revolutionizing artificial intelligence, offering the potential for enhanced learning and complex pattern recognition. However, training QNNs often requires significant computational resources and time, hindering their widespread adoption. This paper introduces an innovative approach to QNNs – the Adaptive Neuron Connection (ANC) – designed to address this challenge by implementing a dynamic adjustment mechanism for neural neuron connections. This system leverages the inherent adaptability of quantum systems to dynamically adjust connection weights based on input signal variations, thereby reducing computational burden and improving model performance. We detail the architecture of the ANC, its operational principles, and preliminary experimental results demonstrating its effectiveness in several benchmark datasets. The core of this design lies in a novel dynamic weight adjustment algorithm that ensures optimal network performance across a wide range of input conditions. This work provides a new framework for QNNs, offering a pathway towards more efficient and scalable learning.

DOI: 10.5281/zenodo.22243958

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