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

2025-07-20

AI Summary

This study addresses three combinatorial optimization problems in machine learning—feature selection, instance selection, and clustering—by unifying their formulations into the Quadratic Unconstrained Binary Optimization (QUBO) framework. It systematically compares quantum annealing (QA) against classical simulated annealing (SA) in solving these QUBO instances. The work introduces a multi-strategy QUBO modeling framework and a heuristic encoding scheme grounded in instance importance. Furthermore, it proposes a novel classical-quantum hybrid clustering pipeline: classical preprocessing generates an initial cluster structure, followed by QA-based optimization of discrete cluster assignments. Experimental results demonstrate that QA achieves faster convergence and superior solution quality in feature selection. In clustering, QA significantly improves cluster compactness (−23.6% average diameter) and retrieval accuracy (+15.4% F1-score), validating the synergistic gains of the hybrid paradigm in both optimization efficiency and solution quality for discrete problems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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.

Problem

• Applies quantum annealing to machine learning optimization problems
• Compares quantum and classical solvers for feature selection efficiency
• Demonstrates QA's effectiveness in clustering and instance selection

Innovation

• Formulate ML tasks as QUBO problems
• Quantum annealing for efficient feature selection
• Classical-to-quantum pipeline for clustering