Quantum Machine Learning Adaptive Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning Adaptive Learning represents a novel approach to machine learning, leveraging the unique properties of quantum mechanics to dynamically adjust the learning process. This paper proposes a new algorithm that utilizes quantum states to represent and evolve features, enabling a more efficient and robust learning process. We detail the core mechanism, showcasing how quantum state evolution drives feature learning and improves model generalization. The research explores the potential benefits of this approach across various machine learning tasks, demonstrating its efficacy through theoretical analysis and preliminary experiments.

DOI: 10.5281/zenodo.22192989

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