Quantum State-Based Adaptive Machine Learning for Enhanced Generalization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum machine learning (QML) is a rapidly developing field that leverages the unique properties of quantum mechanics to enhance machine learning algorithms. This paper explores the application of quantum state-based adaptive learning to improve the generalization capabilities of quantum machine learning models. The core claim is that a novel QML algorithm, dynamically adjusting learning parameters based on input data variations, can significantly improve model performance across diverse datasets. Traditional QML approaches often require manual parameter tuning, limiting adaptability. This research proposes a quantum state-driven learning mechanism that automates parameter adjustments, leading to improved generalization. We present a preliminary implementation demonstrating the potential of this approach to enhance model robustness and predictive accuracy.

DOI: 10.5281/zenodo.22193126

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