Quantum-Inspired Topological Data Analysis for Non-Linear Feature Selection

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of quantum-inspired topological data analysis (TDAN) to enhance feature selection within the context of non-linear data. Traditional feature selection methods often struggle to identify subtle patterns and correlations that are crucial for accurate modeling. We propose a novel approach leveraging quantum-like superposition and entanglement to identify patterns that are difficult to detect classically, thereby improving the accuracy of feature selection. The core mechanism involves manipulating data through quantum-inspired transformations, which, when applied to non-linear datasets, can reveal previously hidden relationships and improve model performance. The paper details the theoretical framework, implementation details, and experimental results demonstrating the efficacy of this quantum-inspired approach.

DOI: 10.5281/zenodo.22510331

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