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Journal of Discrete Mathematical Sciences and Cryptography

Quantum-aided feature selection model – A quantum machine learning approach

Rupam Bhagawati, Thiruselvan Subramanian

Published Jan 1, 2023• 1 citation
Publisher

Abstract

The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by removing noisy and duplicate features through the feature selection method. Although feature selection is an expensive computational process, after many decades, quantum computation tools are in use for many algorithms to implement realistic problems, particularly in the standard of Quantum Annealing. This paper focuses to prospect the standard of quantum computing in order to increase the quality of information classes through the feature selection method. The persuasiveness of the quantum approach is comparable to the classical process that focused on the reliability of quantum methodology from different perspectives.

DOI
10.47974/jdmsc-1735

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The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by removing noisy and duplicate features through the feature selection method. Although feature selection is an expensive computational process, after many decades, quantum computation tools are in use for many algorithms to implement realistic problems, particularly in the standard of Quantum Annealing. This paper focuses to prospect the standard of quantum computing in order to increase the quality of information classes through the feature selection method. The persuasiveness of the quantum approach is comparable to the classical process that focused on the reliability of quantum methodology from different perspectives.
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification.
This paper introduces a novel feature selection algorithm based on quantum machine learning, designed to optimize the selection of relevant features for machine learning models. Traditional feature selection methods often rely on classical computational techniques, which can be limited in their ability to effectively handle complex feature spaces and intricate relationships. This research explores the potential of quantum mechanics to enhance feature selection by leveraging quantum properties, specifically superposition and entanglement, to efficiently explore and prioritize the most impactful features. We present a theoretical framework and initial experimental results demonstrating the algorithm's superior performance in selecting relevant features compared to existing classical methods. The core mechanism centers around quantum-enhanced feature representation and a novel quantum optimization process for feature ranking. This work holds promise for improving the accuracy and efficiency of machine learning models, particularly in high-dimensional datasets.
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Abstract In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.
Quantum-enhanced feature selection is a promising approach to address the challenges posed by high-dimensional data in machine learning. Traditional feature selection methods often struggle to effectively identify the most informative features, leading to suboptimal model performance and increased computational cost. This paper proposes a novel quantum algorithm designed to accelerate and enhance feature selection, offering a significant improvement over existing techniques. We explore the potential of quantum algorithms, specifically focusing on Grover's search algorithm and quantum-enhanced k-means clustering, to achieve efficient identification of relevant features. The core mechanism involves exploiting quantum phenomena like superposition and entanglement to enhance the search space and improve the selection process. We demonstrate the effectiveness of our approach through theoretical analysis and preliminary experiments on benchmark datasets, showcasing its potential to reduce computational cost and improve accuracy in high-dimensional feature selection scenarios.
Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real–world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7–qubit IBM (ibm–perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step.