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arXiv:2205.04346 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi

Published Jul 6, 2022• 47 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1145/3477495.3531755

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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.
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 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.
The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular, quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.
This paper explores the application of quantum algorithms for feature selection and dimensionality reduction within the field of quantum machine learning. Traditional methods for these tasks often suffer from computational bottlenecks, particularly when dealing with high-dimensional datasets. We propose a framework that leverages the principles of quantum computing to accelerate these processes. Specifically, we investigate the transformation of feature selection and dimensionality reduction problems into quantum optimization problems. We then examine the utilization of quantum annealing and variational quantum sampling algorithms for solving these problems. The core claim is that by employing quantum algorithms, we can significantly improve the efficiency and accuracy of machine learning models. The theoretical framework presented outlines a novel approach to tackling these challenges, offering a potential pathway towards more powerful and scalable quantum machine learning systems. This work provides a foundational exploration into this emerging area, focusing on the algorithmic strategies and potential benefits. ---
This paper explores the applications of quantum annealing (QA) and classical simulated annealing (SA) to a suite of combinatorial optimization problems in machine learning, namely feature selection, instance selection, and clustering. We formulate each task as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implement both quantum and classical solvers to compare their effectiveness. For feature selection, we propose several QUBO configurations that balance feature importance and redundancy, showing that quantum annealing (QA) produces solutions that are computationally more efficient. In instance selection, we propose a few novel heuristics for instance-level importance measures that extend existing methods. For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement, and demonstrate consistent improvements in cluster compactness and retrieval metrics. Our results suggest that QA can be a competitive and efficient tool for discrete machine learning optimization, even within the constraints of current quantum hardware.
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.