Quantum Adaptive Machine Learning Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel quantum-enhanced machine learning algorithm, the Quantum Adaptive Machine Learning Algorithm (QAML), designed to address the limitations of traditional machine learning techniques. The core concept of QAML leverages quantum self-adaptation to dynamically adjust model parameters, enabling superior modeling capabilities and improved generalization performance. We detail the algorithm's architecture, the underlying quantum mechanics used for parameter adjustment, and a preliminary evaluation of its performance on a diverse dataset. The paper concludes with a discussion of the potential impact of this approach on various machine learning applications.

DOI: 10.5281/zenodo.22454281

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