基于自适应的量子纠错的拓扑信息提取

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers unprecedented opportunities for data analysis and feature extraction, particularly in complex datasets. This paper explores the application of a novel quantum machine learning approach, termed "Adaptive Quantum Topological Extraction" (AQT), leveraging the unique properties of quantum entanglement to automatically extract intricate topological structures from data. We propose a method that dynamically adjusts optimization parameters based on quantum state changes, enabling efficient and automated extraction of key information. The core mechanism centers around exploiting quantum entanglement to create a feedback loop, iteratively refining the data representation and highlighting crucial topological features. This research aims to advance the state-of-the-art in data analysis through the implementation of a fundamentally new paradigm for topological information extraction. The paper details the theoretical foundation, implementation details, and preliminary results demonstrating the efficacy of AQT in extracting complex topological patterns from a simulated dataset. We highlight the potential for this approach to revolutionize data analysis and feature extraction across a range of applications.

DOI: 10.5281/zenodo.22201002

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