Quantum Entanglement-Based Adaptive Machine Learning for Information Encoding

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing quantum entanglement for adaptive machine learning, specifically focusing on information encoding. The core claim is that leveraging entanglement's unique properties can lead to significantly improved efficiency and accuracy in machine learning models, particularly when dealing with high-dimensional data and complex relationships. The proposed mechanism involves constructing a system capable of real-time measurement and manipulation of entangled states, encoding input data into these states, and employing quantum algorithms, such as Variational Quantum Feature Mapping (VQFM), for learning. Furthermore, an adaptive approach is integrated to optimize the encoding process and feature extraction, capitalizing on the inherent advantages of entanglement. The research addresses a novel area by directly applying entanglement in machine learning rather than solely treating it as a foundational resource for quantum computing, and introduces an adaptive learning framework for enhanced performance. The results presented suggest a promising trajectory for combining quantum mechanics and machine learning to tackle challenging computational problems.

DOI: 10.5281/zenodo.22186310

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