Quantum Machine Learning: Leveraging Quantum Feature Maps for Enhanced Learning

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the application of quantum feature maps in the context of quantum machine learning. The core claim is that effective quantum feature mapping can significantly improve the efficiency and accuracy of machine learning algorithms by transforming classical data into a quantum state representation. We propose a method for designing these feature maps, aiming to bridge the gap between classical and quantum data spaces. The central mechanism involves mapping classical data points to specific quantum states, subsequently leveraging quantum machine learning algorithms for training and prediction. This research contributes to the nascent field of quantum machine learning by exploring the potential of quantum feature maps as a foundational element for achieving enhanced learning capabilities. The study focuses on the theoretical foundations and design principles of these maps, without involving experimental validation.

DOI: 10.5281/zenodo.22137944

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