Quantum Machine Learning: Adaptive Learning for Enhanced Prediction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework, termed Adaptive Learning Quantum Machine Learning (ALQML), designed to facilitate dynamic parameter adjustment during the training process, leading to improved learning efficiency and predictive accuracy. Traditional machine learning often relies on static parameter settings, limiting its adaptability to complex data. ALQML integrates an adaptive learning mechanism, allowing the model to dynamically adjust its parameters based on the data characteristics and training dynamics. This innovative approach aims to overcome these limitations and unlock new possibilities in various prediction and classification tasks. The paper details the framework's architecture, explores the quantum algorithms employed for parameter adjustment, and provides a preliminary assessment of its performance on benchmark datasets.

DOI: 10.5281/zenodo.22196377

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