Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Feature ImportanceSimilar Papers 제목 키워드 기반
A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications
Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has…
BIG-bench Machine LearningQuantum Annealing for Automated Feature Selection in Stress Detection
We present a novel methodology for automated feature subset selection from a pool of physiological signals using Quantum Annealing (QA). As a case study, we will investigate the effectiveness of QA-based feature selectio…
feature selectionQuantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis
Feature selection is vital for identifying relevant variables in classification and regression models, especially in single-cell RNA sequencing (scRNA-seq) data analysis. Traditional methods like LASSO often struggle wit…
feature selectionQuantum Annealing Feature Selection on Light-weight Medical Image Datasets
We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formul…
feature selectionEvaluating the Performance of a D-Wave Quantum Annealing System for Feature Subset Selection in Software Defect Prediction
Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be em…
Prediction