Title: Quantum Machine Learning with Dynamic Embedding Spaces

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum machine learning (QML) presents a revolutionary paradigm shift in the field of artificial intelligence, leveraging the unique properties of quantum mechanics to enhance machine learning algorithms. This paper explores the development of a novel QML algorithm, "Dynamic Embedding Space," designed to address the limitations of traditional approaches by dynamically adapting the embedding space for each data point. We propose a quantum-enhanced transformation that intelligently adjusts the representation of data based on its characteristics, ultimately optimizing learning efficiency and performance across diverse datasets. The core mechanism centers around exploiting quantum entanglement and superposition to create a more flexible and responsive representation, a departure from the static nature of classical embedding techniques. This research focuses on the practical implementation of a quantum algorithm for embedding data, demonstrating its potential to significantly improve the performance of various machine learning tasks.

DOI: 10.5281/zenodo.22481966

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