Quantum Machine Learning: Dynamic Adaptive Learning Framework

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework designed for dynamic data distribution learning and pattern recognition. Traditional machine learning models often struggle with adapting to evolving data distributions, necessitating retraining or complex optimization strategies. This framework leverages quantum computing's inherent dynamic adaptability to achieve rapid learning and improved model accuracy. A key component of this design is the incorporation of quantum entanglement to enhance learning efficiency and model precision. We propose a method utilizing quantum algorithms to effectively capture and model complex data patterns, offering a potential paradigm shift in machine learning capabilities, particularly within dynamic environments. This work explores the potential benefits of this approach and presents preliminary results demonstrating its effectiveness in a specific benchmark dataset.

DOI: 10.5281/zenodo.22265245

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