Quantum Machine Learning Adaptive Parameters

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential for paradigm shifts in algorithm design, surpassing the limitations of classical machine learning. This paper investigates the application of adaptive parameter adjustment within QML algorithms to enhance learning efficiency. We propose a novel quantum algorithm designed to dynamically adjust parameters based on the inherent characteristics of input data, effectively mitigating the curse of dimensionality and improving model robustness. The core mechanism centers around quantum entanglement and superposition to represent and manipulate data effectively, enabling a self-optimizing learning process. The proposed algorithm demonstrates improved performance across a range of benchmark datasets, showcasing the potential for significant gains in learning speed and generalization ability.

DOI: 10.5281/zenodo.22199701

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