Quantum Machine Learning Adaptive Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning Adaptive Learning is a novel approach to quantum machine learning that focuses on dynamically adjusting quantum model parameters to efficiently adapt to new data distributions. This paper proposes a self-adaptive learning algorithm that iteratively refines quantum model parameters based on the observed data distribution, resulting in improved learning efficiency and robustness. The core mechanism involves a continuous parameter optimization process driven by data-driven feedback, circumventing the limitations of fixed parameter sets. Traditional quantum machine learning often necessitates extensive manual parameter tuning, a laborious and time-consuming process. Our algorithm offers a more flexible and adaptive method, promising significant improvements in the speed and effectiveness of quantum machine learning models.

DOI: 10.5281/zenodo.22198907

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