Multi-Body Quantum Entanglement

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental aspect of quantum mechanics, holds immense promise for revolutionizing computation and data processing. This paper investigates the potential of leveraging quantum entanglement in computational architecture, specifically focusing on its application within quantum machine learning. The core claim posits that the unique properties of entanglement, particularly its inherent parallelism and non-classical correlations, can be harnessed to enable significantly enhanced parallel computation capabilities, and this capability will be utilized to advance quantum machine learning algorithms. This work explores the creation of quantum entanglement as a foundational element for computation, details quantum algorithms designed to exploit entanglement, and investigates the potential advantages quantum machine learning offers compared to classical approaches. The paper presents a novel framework for designing quantum algorithms centered around entanglement, demonstrating preliminary results that suggest a significant shift in the landscape of parallel computation, with a particular emphasis on quantum machine learning.

DOI: 10.5281/zenodo.22289621

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