Quantum Machine Learning with Multi-Qubit Entanglement

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing multi-qubit entanglement for enhancing the performance of quantum machine learning (QML) algorithms. Current QML approaches often face limitations in data representation and feature extraction, particularly when dealing with high-dimensional datasets. We propose a novel framework that leverages the inherent properties of multi-qubit entanglement to address these challenges. The core idea involves designing an algorithm that exploits entanglement for data compression and feature extraction, leading to improved computational efficiency and potentially increased accuracy. Specifically, we explore the use of entangled states to create richer data representations and perform more efficient pattern recognition. The theoretical framework is presented, outlining the key components and demonstrating the potential advantages of this approach. While a full experimental implementation is not presented due to the current limitations of hardware, we provide a detailed analysis and a roadmap for future research in this area. The central claim of this work is that utilizing multi-qubit entanglement can significantly improve the performance of QML algorithms.

DOI: 10.5281/zenodo.22315448

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