Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2507.15063

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

Pomeroy, Chloe, Aleksandar Pramov, Karishma Thakrar, Lakshmi Yendapalli

Published Jul 20, 2025• 0 citations
arXivHTMLPDF

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.

Related papers

Quantum Annealing based Feature Selection in Machine Learning

Daniel Pranjić, Bharadwaj Chowdary Mummaneni, Christian Tutschku

Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature selection process in an ML-pipeline, used for maximizing the mutual information (MI) or conditional mutual information (CMI) of the underlying feature space. Calculating the optimal set of features that maximize the MI or CMI is computationally intractable for large datasets on classical computers, even with approximative methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. We demonstrate the capability of this approach to identify the best feature combinations that maximize the MI or CMI. To showcase its real-world applicability, we solve the MIQUBO problem to forecast the prices of used excavators. Our results demonstrate that for datasets with a small MI concentration the MIQUBO approach can provide a significant improvement over MI-only based approaches, dependent on the dimension of the selected feature space.

arXiv:2411.19609•Nov 29, 2024•0 citations

Feature selection on quantum computers

Sascha Mücke, Raoul Heese, Sabine Müller +2 more

Abstract In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.

arXiv:2203.13261•Quantum Machine Intelligence•Feb 20, 2023•61 citations

Quantum Annealing Combinatorial Optimization (QACO)

Jincheng Zhang

This paper explores the application of Quantum Annealing (QA) to the field of combinatorial optimization. The core idea is to leverage the principles of quantum mechanics, specifically quantum tunneling, to efficiently solve complex optimization problems. We present a framework for utilizing QA to tackle problems represented as quadratic unconstrained binary optimization (QUBO) models. The methodology focuses on mapping the problem's constraints and objective function into a form suitable for QA hardware. We discuss the challenges involved in this mapping process, including the limitations of current QA devices and the need for careful problem formulation. Furthermore, we analyze the potential advantages of QA over classical optimization techniques, particularly concerning the exploration of a wider solution space. The research highlights the promise of QACO as a viable approach for tackling computationally demanding combinatorial optimization tasks, offering a novel method to enhance optimization efficiency.

•Sep 3, 2026•0 citations

Quantum Annealing for Optimal Feature Subset Selection for Enhanced Medical Classification

Abhishek Bhattacharya, Blerta Prevalla Etemi, Debabrata Samanta

The rapid explosion of medical image data poses a significant challenge to reliable disease Classification with high-dimensional feature spaces and intricate inter-feature correlations. In this paper, a new quantum annealing-inspired framework for optimal feature subset selection, specifically tailored to improve medical image Classification. In contrast to traditional filter, wrapper, or embedded techniques that tend to ignore redundancy and feature interactions, our solution poses feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it effectively using D-Wave quantum annealer hardware. The presented approach makes three primary contributions: (i) a QUBO formulation that captures feature importance, redundancy suppression, and interaction synergy simultaneously, (ii) a hybrid quantum-classical pipeline with embedding, decomposition, and chain strength optimization for efficient execution on existing quantum hardware, and (iii) a comprehensive evaluation on three heterogeneous medical imaging datasets—diabetic retinopathy, breast cancer histopathology, and chest X-ray pneumonia. Experimental findings exhibit uniform improvements in Classification performance (up to 12.1%), drastic feature reduction (65–80%), and much reduced computational time over six state-of-the-art classical approaches. With the exposure of clinically relevant features and robust statistical significance across benchmarks, our approach places quantum annealing as an influential and scalable tool for future medical AI applications.

Procedia Computer Science•Jan 1, 2026•0 citations

Analyzing the effectiveness of quantum annealing with meta-learning

Riccardo Pellini, Maurizio Ferrari Dacrema

Abstract The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, but there is not yet a clear understanding of which are the characteristics of a problem that make it difficult to solve with QA. In this work, we propose a new methodology to study the effectiveness of QA based on meta-learning models. To do so, we first build a dataset composed of more than five thousand instances of ten different optimization problems. We define a set of more than a hundred features to describe their characteristics and solve them with both QA and three classical solvers. We publish this dataset online for future research. Then, we train multiple meta-models to predict whether QA would solve that instance effectively and use them to probe which features with the strongest impact on the effectiveness of QA. Our results indicate that it is possible to accurately predict the effectiveness of QA, validating our methodology. Furthermore, we observe that the distribution of the problem coefficients representing the bias and coupling terms is very informative in identifying the probability of finding good solutions, while the density of these coefficients alone is not enough. The methodology we propose allows to open new research directions to further our understanding of the effectiveness of QA, by probing specific dimensions or by developing new QUBO formulations that are better suited for the particular nature of QA. Furthermore, the proposed methodology is flexible and can be extended or used to study other quantum or classical solvers.

arXiv:2408.00570•Quantum Machine Intelligence•Jul 25, 2024•5 citations

Parametric Quantum Feature Selection Methods for Fraud and Default Detection

Sutapa Samanta, Dagen Wang, Todd Hodges +1 more

Feature selection plays an important role in improving the efficiency of machine learning models for credit card fraud and default detection. We formulate the feature selection problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which we solve using quantum annealers. We propose three new formulations based on this framework that improve the efficiency and flexibility of machine learning models. We benchmark the proposed methods, existing approaches from the literature, and also compare with classical feature-selection methods such as Random Forest feature importance and a combination of mutual information and Spearman correlation. Extensive experiments show that feature selection using quantum computers consistently performs better than the classical methods. Our experiments show the promise of using quantum computers in machine learning tasks in financial risk assessment applications.

Proceedings of the AAAI Symposium Series•Nov 23, 2025•2 citations
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.
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature selection process in an ML-pipeline, used for maximizing the mutual information (MI) or conditional mutual information (CMI) of the underlying feature space. Calculating the optimal set of features that maximize the MI or CMI is computationally intractable for large datasets on classical computers, even with approximative methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. We demonstrate the capability of this approach to identify the best feature combinations that maximize the MI or CMI. To showcase its real-world applicability, we solve the MIQUBO problem to forecast the prices of used excavators. Our results demonstrate that for datasets with a small MI concentration the MIQUBO approach can provide a significant improvement over MI-only based approaches, dependent on the dimension of the selected feature space.
Abstract In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.
This paper explores the application of Quantum Annealing (QA) to the field of combinatorial optimization. The core idea is to leverage the principles of quantum mechanics, specifically quantum tunneling, to efficiently solve complex optimization problems. We present a framework for utilizing QA to tackle problems represented as quadratic unconstrained binary optimization (QUBO) models. The methodology focuses on mapping the problem's constraints and objective function into a form suitable for QA hardware. We discuss the challenges involved in this mapping process, including the limitations of current QA devices and the need for careful problem formulation. Furthermore, we analyze the potential advantages of QA over classical optimization techniques, particularly concerning the exploration of a wider solution space. The research highlights the promise of QACO as a viable approach for tackling computationally demanding combinatorial optimization tasks, offering a novel method to enhance optimization efficiency.
The rapid explosion of medical image data poses a significant challenge to reliable disease Classification with high-dimensional feature spaces and intricate inter-feature correlations. In this paper, a new quantum annealing-inspired framework for optimal feature subset selection, specifically tailored to improve medical image Classification. In contrast to traditional filter, wrapper, or embedded techniques that tend to ignore redundancy and feature interactions, our solution poses feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it effectively using D-Wave quantum annealer hardware. The presented approach makes three primary contributions: (i) a QUBO formulation that captures feature importance, redundancy suppression, and interaction synergy simultaneously, (ii) a hybrid quantum-classical pipeline with embedding, decomposition, and chain strength optimization for efficient execution on existing quantum hardware, and (iii) a comprehensive evaluation on three heterogeneous medical imaging datasets—diabetic retinopathy, breast cancer histopathology, and chest X-ray pneumonia. Experimental findings exhibit uniform improvements in Classification performance (up to 12.1%), drastic feature reduction (65–80%), and much reduced computational time over six state-of-the-art classical approaches. With the exposure of clinically relevant features and robust statistical significance across benchmarks, our approach places quantum annealing as an influential and scalable tool for future medical AI applications.
Abstract The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, but there is not yet a clear understanding of which are the characteristics of a problem that make it difficult to solve with QA. In this work, we propose a new methodology to study the effectiveness of QA based on meta-learning models. To do so, we first build a dataset composed of more than five thousand instances of ten different optimization problems. We define a set of more than a hundred features to describe their characteristics and solve them with both QA and three classical solvers. We publish this dataset online for future research. Then, we train multiple meta-models to predict whether QA would solve that instance effectively and use them to probe which features with the strongest impact on the effectiveness of QA. Our results indicate that it is possible to accurately predict the effectiveness of QA, validating our methodology. Furthermore, we observe that the distribution of the problem coefficients representing the bias and coupling terms is very informative in identifying the probability of finding good solutions, while the density of these coefficients alone is not enough. The methodology we propose allows to open new research directions to further our understanding of the effectiveness of QA, by probing specific dimensions or by developing new QUBO formulations that are better suited for the particular nature of QA. Furthermore, the proposed methodology is flexible and can be extended or used to study other quantum or classical solvers.
Feature selection plays an important role in improving the efficiency of machine learning models for credit card fraud and default detection. We formulate the feature selection problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which we solve using quantum annealers. We propose three new formulations based on this framework that improve the efficiency and flexibility of machine learning models. We benchmark the proposed methods, existing approaches from the literature, and also compare with classical feature-selection methods such as Random Forest feature importance and a combination of mutual information and Spearman correlation. Extensive experiments show that feature selection using quantum computers consistently performs better than the classical methods. Our experiments show the promise of using quantum computers in machine learning tasks in financial risk assessment applications.