Quantum Machine Learning: Adaptive Learning Strategies

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum machine learning (QML) has emerged as a promising field with the potential to revolutionize various computational tasks, particularly those involving complex data and non-linear relationships. This paper presents a novel quantum machine learning algorithm, termed "Adaptive Quantum Learning," designed to address the limitations of traditional quantum algorithms by incorporating a dynamically adjusted learning strategy. The core mechanism of this algorithm centers around the continuous adaptation of learning parameters based on data fluctuations, leading to improved learning efficiency and generalization performance. We explore the theoretical foundations underpinning this approach and provide a preliminary implementation demonstrating its effectiveness on a simplified benchmark dataset. The study highlights the potential of this adaptive strategy to overcome the challenges faced by existing QML methods in dynamic environments.

DOI: 10.5281/zenodo.22154306

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