Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering

TL;DR

This paper demonstrates how quantum annealing can be effectively applied to feature selection, instance selection, and clustering in machine learning by formulating these tasks as QUBO problems and comparing quantum and classical solutions.

Contribution

It introduces novel QUBO formulations for these tasks and shows that quantum annealing offers computational advantages and improved clustering quality over classical methods.

Findings

Quantum annealing outperforms classical simulated annealing in feature selection.

Proposed heuristics improve instance importance measures.

QUBO-based clustering enhances cluster compactness and retrieval metrics.

Abstract

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.