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Knowledge and Information Systems

Quantum computing and quantum-inspired techniques for feature subset selection: a review

Ashis Kumar Mandal, Basabi Chakraborty

Published Nov 17, 2024• 20 citations
Publisher
DOI
10.1007/s10115-024-02282-5

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Feature selection is a critical step in many machine learning pipelines, aiming to reduce dimensionality and improve model performance by identifying the most relevant features from a large set of potential predictors. Traditional feature selection methods often struggle with the computational complexity that arises in high-dimensional data. This paper explores the application of quantum-inspired algorithms to feature selection, drawing inspiration from concepts like superposition and interference. We propose a novel approach that represents feature subsets as quantum states and utilizes interference patterns to prioritize features exhibiting stronger correlations with the target variable. Our method leverages probabilistic modeling and Monte Carlo simulations to approximate the quantum interference process, offering a potentially efficient alternative to exhaustive search and some traditional feature selection techniques. The core claim of this work is that quantum concepts can be effectively employed to accelerate feature selection in high-dimensional data. We demonstrate the feasibility of this approach through a theoretical framework and outline the key elements required for its practical implementation.
High-dimensional feature selection remains a critical challenge in machine learning, particularly when dealing with datasets containing a large number of potential predictive variables. Traditional Bayesian approaches often struggle with this complexity due to the computational demands of exhaustive search or approximate inference methods. This paper proposes a novel approach leveraging quantum-inspired techniques to accelerate the feature selection process. We simulate a quantum annealing process, representing feature selection as a probability distribution. Features are initially encoded as quantum bits (qubits), and interference patterns are manipulated to amplify the probabilities associated with relevant feature subsets, guided by a Bayesian prior. The core of this method is the exploitation of quantum superposition and interference to explore the feature space more efficiently than classical methods. The proposed approach aims to provide a computationally feasible solution for high-dimensional feature selection while maintaining the accuracy and interpretability inherent in Bayesian inference. The evaluation of the proposed method demonstrates its potential benefits in terms of speed and accuracy compared to traditional Bayesian methods, particularly when the number of features is large.