Quantum Adaptive Machine Learning Model

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced machine learning model, the Quantum Adaptive Machine Learning Model (QAML), designed to address the limitations of traditional machine learning algorithms. The QAML leverages quantum self-adaptation to dynamically adjust model parameters based on training data, fostering improved generalization and adaptability. We present a theoretical framework detailing the core mechanism of this adaptation, demonstrating its potential to overcome existing challenges in machine learning. The paper concludes with a discussion of the model's architecture and potential applications.

DOI: 10.5281/zenodo.22331543

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