Quantum Adaptive Machine Learning Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework, dubbed "Quantum Adaptive Machine Learning," designed to dynamically adjust model parameters based on the input data's probabilistic distribution. Traditional machine learning approaches often struggle with data distribution shifts, necessitating manual parameter tuning. Our framework leverages adaptive adjustment, mimicking data distribution to optimize model performance. The core mechanism involves simulating data distributions and employing machine learning to enhance model adaptability. This results in a more robust and responsive learning process, particularly effective when data characteristics evolve over time. The research demonstrates the framework's ability to achieve superior accuracy and generalization compared to traditional methods in a series of benchmark datasets.

DOI: 10.5281/zenodo.22195625

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