Adaptive Quantum Neural Networks: Dynamic Architecture Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum neural network architecture optimization algorithm, termed "Quantum Self-Adaptation Adjustment" (QSAA). We propose a novel approach to automatically tune the network's structure and parameters, leveraging the inherent dynamic properties of quantum neural networks. The core mechanism centers around a "Quantum Self-Adaptation Adjustment" (QSAA) process, which employs quantum computing principles to learn more complex network configurations and parameter adjustments. The algorithm's design aims to improve model performance and generalization capabilities compared to traditional neural network approaches. We present preliminary results demonstrating the effectiveness of QSAA in optimizing network structure for a specific classification task, highlighting its potential for enhancing deep learning models.

DOI: 10.5281/zenodo.22203331

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