Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering
It addresses optimization challenges in machine learning for researchers and practitioners, but it is incremental as it builds on existing quantum annealing techniques.
This paper tackles combinatorial optimization problems in machine learning, such as feature selection and clustering, by applying quantum annealing and classical methods, showing that quantum annealing offers computationally more efficient solutions and consistent improvements in cluster metrics.
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.