Quantum Machine Learning: Adaptive Feature Selection Framework

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework for adaptive feature selection, designed to automatically learn crucial features and significantly enhance the performance of machine learning models. Traditional feature selection methods often rely on manual design, limiting adaptability and potentially hindering model optimization. We propose a quantum algorithm that dynamically learns and adjusts feature selection strategies, offering a promising approach to improve model accuracy and robustness. The framework leverages the unique properties of quantum mechanics to efficiently explore feature spaces and identify key elements crucial for classification and regression tasks. This work presents a theoretical analysis and initial experimental validation demonstrating the superior performance of our approach compared to existing techniques. The core mechanism focuses on quantum-enhanced feature representation learning and adaptive selection, ultimately contributing to a more intelligent and adaptable machine learning system.

DOI: 10.5281/zenodo.22242135

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