Title: Adaptive Quantum Feature Extraction

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper presents an innovative approach to quantum data analysis, focusing on the automated extraction of key features through the application of a quantum algorithm. The core aim is to improve the efficiency of data processing by leveraging the inherent properties of quantum systems to identify subtle patterns and correlations within data. We introduce a novel system that dynamically adapts to the data's characteristics, ensuring optimal feature extraction for a wide range of quantum datasets. The system employs a quantum algorithm specifically designed for pattern recognition, resulting in a significantly enhanced analysis process. This research addresses a critical challenge in quantum data science – the need for efficient and automated feature discovery – offering a potential paradigm shift in data processing methodologies.

DOI: 10.5281/zenodo.22250847

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