Adaptive Architecture for Quantum Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) holds immense promise for revolutionizing various fields, particularly in complex data analysis and optimization. Traditional machine learning approaches often struggle to effectively handle data variations and complex relationships. This paper introduces an Adaptive Architecture for Quantum Machine Learning (A-QML) – a novel algorithm framework designed to dynamically adjust quantum model parameters and structures based on the input data's characteristics. The core mechanism centers around leveraging quantum machine learning's inherent adaptability to improve model generalization and efficiency. We present a detailed explanation of the architecture, its operational principles, and preliminary results demonstrating its effectiveness in a specific, yet illustrative, QML task. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22297384

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