Quantum Machine Learning Adaptive Complexity Adjustment

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel quantum machine learning algorithm, Quantum Machine Learning Adaptive Complexity Adjustment (QML-MAC), designed to automatically adjust the complexity of a learning process based on the characteristics of the input data. Traditional machine learning algorithms frequently employ fixed parameters, which can limit the algorithm's effectiveness. QML-MAC leverages quantum mechanics principles to dynamically modify the complexity of the learning process, thereby improving learning efficiency and potentially unlocking new capabilities. This research explores the potential of quantum algorithms to enhance optimization and data handling, specifically focusing on adapting to the inherent complexities within datasets. The core mechanism centers around a quantum-enhanced learning loop that iteratively adjusts the complexity, leading to improved performance and reduced computational resources.

DOI: 10.5281/zenodo.22199766

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