Quantum Machine Learning Geometric Feature Extraction

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a rapidly advancing field, offering potential breakthroughs in various computational tasks, including machine learning. This paper investigates the application of geometric feature extraction to Quantum Machine Learning, specifically focusing on enhancing the accuracy and efficiency of machine learning models. We propose a novel framework leveraging quantum machine learning algorithms for geometric feature extraction, aiming to overcome the limitations of traditional methods. The core claim is that this framework can significantly improve the performance of machine learning models by effectively extracting and utilizing geometric features, leading to enhanced accuracy and computational efficiency. Traditional machine learning relies heavily on handcrafted features, often failing to capture the intricate relationships within data. Quantum Machine Learning, with its inherent ability to handle complex data relationships, offers a potential solution for this limitation. This work explores the potential of quantum algorithms for geometric feature representation and extraction, demonstrating a promising path toward more sophisticated and efficient machine learning models.

DOI: 10.5281/zenodo.22264225

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