基于自适应的量子机器学习的拓扑信息提取

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of a novel quantum machine learning framework, termed "Adaptive Quantum Machine Learning" (AQML), for the extraction of crucial topological information from complex datasets. Traditional data analysis and feature extraction methods often rely on manual intervention, limiting efficiency and potentially introducing human bias. AQML leverages quantum computing's inherent parallelism to automatically identify and isolate key topological features within datasets, dramatically enhancing the speed and accuracy of analysis. We present a detailed implementation of the AQML algorithm, outlining its architecture, computational methodology, and validation results. This approach promises to revolutionize data analysis by automating the extraction of critical topological properties, enabling more efficient and insightful data exploration. This work is motivated by the need for more robust and adaptable methods in the analysis of complex, high-dimensional datasets, particularly in fields such as materials science, biology, and network analysis.

DOI: 10.5281/zenodo.22200846

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