Scientific Reports· 2026Q1
A quantum computing-based approach for feature selection in regression models
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- Q1SCImago
- 2026year
Short summary
A new QUBO-based feature selection method for regression models, compatible with quantum computers, effectively captures complex feature interactions and yields more compact feature subsets compared to traditional methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces a QUBO-based feature selection formulation for regression, modeling linear and non-linear dependencies.
- The method is compatible with quantum computing (QA-compatible).
- Evaluated on Wine Quality, Insurance, and Bike Sharing System datasets.
- Achieved competitive predictive performance and more compact feature subsets than Mutual Information and MI_QC baseline.
AI-generated from the title and abstract; the full text is not read.
Abstract
Feature Selection (FS) is an essential data preprocessing technique aimed at identifying the most relevant features for predictive models. However, traditional FS approaches often struggle to capture complex interactions in real-world datasets, limiting their ability to fully support high-performing Machine Learning (ML) models. This study introduces a QA-compatible QUBO-based FS formulation for regression that models both linear and non-linear feature dependencies through hybrid linear and quadratic coefficients. Using three datasets; Wine Quality, Insurance, and Bike Sharing System (BSS) we evaluate the method across multiple predictive models to assess its robustness and generalizability. Results show competitive predictive performance and, in several settings, more compact feature subsets than Mutual Information and the MI_QC baseline. This study introduces a methodological refinement over previous QUBO-based FS methods and demonstrates practical applicability through statistically robust benchmarking.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science