Quantum-Inspired Algorithms for Feature Selection in Big Data

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Feature selection is a crucial step in many machine learning applications, aiming to reduce dimensionality and improve model performance by identifying the most relevant features from a dataset. However, traditional feature selection methods often suffer from computational complexity, particularly when dealing with 'big data' characterized by a large number of features. This paper explores the application of quantum-inspired algorithms to address this challenge. Specifically, we investigate the use of techniques inspired by quantum amplitude estimation to accelerate the feature selection process. Our approach leverages quantum-like phenomena, such as superposition and interference, to efficiently evaluate feature relevance. We demonstrate that these algorithms can offer significant speedups compared to conventional methods, especially for datasets with a high dimensionality. The core claim is that traditional feature selection methods are computationally expensive for large datasets, and the proposed quantum-inspired algorithms provide an efficient alternative. We present a framework for implementing these algorithms and discuss their potential impact on various machine learning tasks.

DOI: 10.5281/zenodo.22293455

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