基于量子机器学习的非线性几何建模

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a transformative field, offering potential solutions to complex problems across diverse disciplines. This paper explores the application of QML to the modeling of non-linear geometric data, focusing on the development of a novel approach that leverages quantum properties to enhance model accuracy and efficiency. We introduce a new methodology incorporating quantum algorithms for data representation and feature extraction, ultimately aiming to provide a more precise and insightful representation of geometric datasets. The core claim of this work is that integrating quantum machine learning with non-linear geometric modeling will yield significant advancements in data analysis and prediction.

DOI: 10.5281/zenodo.22189350

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