基于自适应的量子机器学习的概率模型优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential for significant advancements in computational capabilities, particularly in areas requiring complex data analysis and pattern recognition. However, the inherent complexity of quantum algorithms often necessitates careful parameter optimization, a process traditionally reliant on human intervention. This paper presents a novel approach to quantum machine learning – a probabilistic model optimization algorithm based on adaptive parameter adjustment – designed to dramatically improve training efficiency and model performance. We introduce a method that dynamically adjusts the parameters of a probabilistic model, leveraging self-tuning capabilities to achieve a more robust and efficient training cycle. This represents a significant step forward, addressing a critical bottleneck in the development of quantum machine learning models.

DOI: 10.5281/zenodo.22246078

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