Quantum Machine Learning: Feature Extraction and Representation Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores a novel approach to feature extraction and representation learning within the context of quantum machine learning. The core claim is to enhance the performance and efficiency of machine learning algorithms by leveraging quantum computation for these foundational tasks. We introduce a methodology that utilizes quantum algorithms to transform raw data into representations optimized for subsequent machine learning processes. This represents a new direction in the burgeoning field of quantum machine learning, offering potential advantages over classical methods, particularly for complex datasets and computationally intensive tasks. The paper details the theoretical framework, outlines the key components of the proposed system, and discusses potential applications and future research directions. The primary focus is on the algorithmic design and mathematical foundations underpinning this approach, aiming to provide a solid basis for future development and experimentation.

DOI: 10.5281/zenodo.22245638

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