Dynamic Topology Transformation Feature Extraction

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to feature extraction based on quantum entanglement and quantum gates, designed to automatically identify and quantify dynamic topology transformations. The core principle lies in leveraging the unique properties of quantum mechanics to achieve a more efficient and precise representation of complex geometric processes. Traditional feature extraction methods often struggle with the intricacies of dynamic topology, necessitating a fundamentally different algorithmic strategy. This work proposes a quantum-enhanced feature extraction pipeline that promises improved accuracy and speed in geometric modeling and simulation. The proposed method utilizes quantum entanglement to create a correlated quantum state, which is then manipulated by quantum gates to reveal underlying topological features. We demonstrate the effectiveness of this approach through illustrative examples, showcasing its potential to accelerate and enhance geometric analysis. The focus is on providing a robust and adaptable framework for extracting crucial topological information from dynamic datasets.

DOI: 10.5281/zenodo.22272074

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