Quantum Machine Learning with Multi-Variable Entangled Encoding

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the potential of multi-variable entangled encoding as a novel approach to enhance quantum machine learning algorithms. The core claim is that exploiting multi-variable entanglement can significantly improve the parallel computation capabilities and information storage efficiency within these algorithms. The proposed mechanism involves encoding multiple quantum bits into multi-variable entangled states. Leveraging the inherent correlations within these entangled states allows for parallel computations and information compression. We explore theoretical frameworks and mathematical formulations to demonstrate the feasibility and potential advantages of this technique. The research provides a new perspective on optimizing quantum machine learning algorithms, addressing limitations in current approaches and paving the way for more efficient quantum data processing. This work focuses on the fundamental principles and theoretical underpinnings, without requiring experimental validation.

DOI: 10.5281/zenodo.22337747

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