基于量子纠缠的自适应特征匹配

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel approach to feature matching leveraging quantum entanglement. Traditional feature matching methods often suffer from limitations in handling complex, high-dimensional data and can be computationally expensive. We propose a new method, Quantum Entanglement Feature Matching (QEMF), that utilizes quantum entanglement to establish a correlated quantum state between data points, enabling a more precise and efficient matching process. The core mechanism hinges on the ability to create and manipulate entangled states to identify and align features across data points, potentially surpassing the capabilities of classical methods. We demonstrate the effectiveness of QEMF through rigorous analysis and simulations, showcasing significant improvements in accuracy and speed compared to existing techniques. The study also explores the potential of QEMF to address challenges in specific domains, including image recognition and natural language processing. This research offers a fundamentally new perspective on feature matching, potentially unlocking significant advancements in machine learning.

DOI: 10.5281/zenodo.22296688

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