Quantum-Inspired Neural Networks for Feature Selection

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the application of quantum-inspired neural networks (QINNs) for feature selection, addressing the limitations of classical feature selection techniques in high-dimensional datasets. Traditional methods frequently struggle to pinpoint truly relevant features, often relying on heuristics or statistical measures that can be misleading. We propose leveraging quantum-inspired algorithms, specifically amplitude amplification, to efficiently identify the most pertinent features. The core idea is to mimic the principles of quantum measurement, where a superposition of states collapses into a single, highly probable outcome. QINNs utilize a network architecture designed to emulate this process, iteratively amplifying the amplitudes associated with the relevant features while suppressing those of the irrelevant ones. The resulting feature selection process demonstrates the potential for improved accuracy and efficiency compared to conventional methods. We present a detailed framework for implementing QINNs for feature selection, outlining the network architecture, training methodology, and evaluation metrics. The theoretical underpinning of this approach is discussed, highlighting the connection to quantum measurement theory. The results of simulations and theoretical analysis demonstrate the efficacy of this method in identifying key features and reducing dimensionality while maintaining predictive performance.

DOI: 10.5281/zenodo.22241215

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