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

Discover Artificial Intelligence

Improving dimensionality-reduction for carbon-emissions prediction using hybrid quantum annealing-based feature selection and PCA

Ghifari Munawar, Nur Ulfa Maulidevi, Kridanto Surendro

Published Sep 1, 2026• 0 citations
Publisher

Abstract

Accurate carbon-emissions prediction is critical for evidence-based climate policy, where even modest improvements can influence emission–reduction targets. However, high-dimensional datasets often exhibit noise, nonlinear dependencies, and multicollinearity that degrade model stability. We propose QMICO-PCA, a hybrid dimensionality-reduction method that integrates quantum annealing (QA)-based feature selection with principal component analysis (PCA). In the selection stage, mutual information (MI) and Pearson correlation (CO) are combined multiplicatively within a QUBO formulation to jointly reward nonlinear relevance and penalize linear redundancy, enabling balanced subset optimization. The selected features are then transformed via PCA to enhance generalization and mitigate overfitting. We evaluate QMICO-PCA against classical approaches (RFE, LASSO, ELN), with and without PCA, using three regression models on the Our World in Data carbon dataset (13,706 records; 79 features; 1970–2023). Experiments are conducted on heterogeneous QA-based solvers to assess optimization robustness. QMICO-PCA achieves the strongest overall performance (average RMSE = 1134.12, MAE = 237.54, $$R^2$$ = 0.8020), corresponding to a 4-7% error reduction over classical methods. In multi-year emission prediction, such reductions can lead to lower cumulative projection errors, supporting more reliable quantitative policy design. Statistical analysis confirms significance (Friedman $$p=0.0018$$ ) with a strong effect size ( $$W=0.975$$ ) and significant Holm-adjusted post-hoc comparisons ( $$p_{\text {holm}}<0.05$$ ). As additional validation, the proposed method is further evaluated on small-scale carbon-emissions datasets, where it also demonstrates consistent performance. Overall, these findings suggest that the proposed method provides a competitive approach for high-dimensional carbon-emissions modeling.

DOI
10.1007/s44163-026-02089-z

Related papers

Proposed Feature Dimensionality Reduction Method in a Predictive Model for Carbon Neutrality: A Hybrid Quantum-Classical Approach

Ghifari Munawar, Nur Ulfa Maulidevi, Kridanto Surendro

As global carbon emissions continue to rise, achieving carbon neutrality has become a primary objective worldwide. Predictive modeling for carbon emissions has emerged as a crucial strategy, with numerous methods developed to forecast and manage emissions effectively. Traditional models, which predominantly employ classical computing-based metaheuristic optimization techniques, often face limitations in reaching global optima, impacting prediction accuracy. This study introduces a hybrid quantum-classical approach to enhance prediction models, particularly through feature dimensionality reduction, which is essential for improving model performance. Quantum algorithms, especially quantum annealing (QA), have shown advantages in addressing complex optimization problems by utilizing principles such as superposition and tunneling, thus offering significant benefits over classical methods. The integration of QA with classical dimensionality reduction techniques, such as principal component analysis (PCA), represents a novel advancement in managing high-dimensional data within predictive models. By optimizing feature selection, our approach aims to increase both the accuracy and computational efficiency of carbon neutrality models. This paper proposes hybrid quantum-classical optimization opportunities that can serve as a foundation for future studies, ultimately leading to more robust and accurate predictive models.

International Conference on Software and Computer Applications (ICSCA)•Feb 20, 2025•1 citation

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

A Hybrid Dimensionality Reduction and Quantum Support Vector Machine for Breast Cancer Diagnosis

Hằng Đặng, My Nguyen, Van Tran +1 more

Quantum Support Vector Machines (QSVMs) have recently emerged as a promising approach for biomedical classification tasks. However, their practical deployment remains constrained by limited qubit availability and sensitivity to high-dimensional feature spaces. This study proposes a hybrid framework integrating Pearson correlation–based feature selection and Principal Component Analysis (PCA) with QSVM for breast cancer diagnosis. First, Pearson correlation analysis is employed to remove redundant and weakly relevant features. Subsequently, PCA projects the selected attributes into a compact subspace while preserving most of the original data variance. This dimensionality reduction strategy decreases the number of qubits required for quantum encoding and improves computational efficiency. Experiments conducted on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the proposed hybrid QSVM achieves 98% classification accuracy, outperforming or matching existing classical and quantum-based approaches. The results confirm that combining classical preprocessing techniques with quantum classifiers provides a robust and resource-efficient solution for biomedical data analysis.

Smart Systems and Devices•May 6, 2026•0 citations

Quantum annealing-based feature selection

Daniel Pranjić, Bharadwaj Chowdary Mummaneni, Christian Tutschku

Feature selection is crucial for enhancing the accuracy and efficiency of machine learning models. This work investigates the possibility of the usefulness of a quantum annealer to tackle the challenge of selecting features based on maximizing mutual information (MI) and conditional mutual information (CMI). Calculating the optimal feature set for maximum MI and CMI remains computationally intractable for large datasets on classical computers, even with approximation methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. Importantly, the study demonstrates the relevancy of this approach in identifying the best feature combinations that maximizes the MI and CMI. To showcase its real-world applicability, we apply MIQUBO to forecasting the price of used excavators. Our results demonstrate that using the MIQUBO approach there is an improvement in the prediction of machine learning models for datasets, with a smaller MI concentration on a subset of all features. • Problem Addressed: We tackle the computationally difficult problem of feature selection for machine learning models, specifically by maximizing mutual information (MI) and conditional mutual information (CMI) between features and the target variable. • Proposed Solution: We employ the adiabatic quantum computation paradigm to tackle this problem. The study utilizes a hybrid annealing approach, specifically the D-Wave Kerberos framework, which combines classical and quantum algorithms to solve the complex optimization problem. The feature selection task is reformulated as a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) problem, which can be solved on a quantum annealer. • Real-World Application: The MIQUBO method is applied to a practical, real-world scenario: forecasting the prices of used excavators. • Key Findings: The research demonstrates that using the MIQUBO approach leads to an improvement in the prediction performance of machine learning models. This is particularly true for datasets where the mutual information is less concentrated on a small subset of features.

Neurocomputing•Oct 15, 2025•0 citations

A quantum computing-based approach for feature selection in regression models

Amirhossein Nourbakhshrezaei, Soroush Sheikh Gargar, Mojgan Jadidi

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.

Scientific Reports•Sep 17, 2026•0 citations

Compress, Encode, Diagnose: PCA-Enhanced QNNs for Breast Cancer Classification

Aman Kumar Prajapati, Lupam K Saha, Mohammed Nabeel Ahsan +1 more

Quantum neural networks (QNNs) offer a promising avenue for machine learning by leveraging quantum computation, but practical QNN models are constrained by the low dimensionality of the input data they can handle. This paper proposes a hybrid quantum-classical neural network architecture that incorporates principal component analysis (PCA) for dimensionality reduction and variational quantum circuits (VQC) for quantum feature encoding. Focusing on the Wisconsin Diagnostic Breast Cancer dataset (569 instances, 30 features) as a representative biomedical classification task, in this work, we design hybrid quantum-classical neural network (QNN) models using 5-qubit and 10-qubit quantum circuits. For comparison, we use classical baseline identical to the downstream head but without the VQC+measurement. The preprocessed inputs feed the classifier head directly; only the input layer dimension is adjusted to the number of features. The $\mathbf{5}$-qubit hybrid model is evaluated under two conditions: with and without principal component analysis (PCA) applied as a preprocessing step to investigate the effect of dimensionality reduction on model performance. In contrast, the 10 -qubit hybrid model is evaluated without PCA, directly processing the input features to explore its capacity to handle higher-dimensional data within the quantum circuit. Performance is measured in terms of classification accuracy, precision, recall, and F1-score. We evaluate the model’s robustness by injecting noise into the training data, finding that the PCA-enhanced hybrid QNN maintains competitive accuracy and resilience under perturbation. This study highlights the potential of PCA-driven variational quantum encodings to enhance QNNs for reliable biomedical data classification.

IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)•Nov 4, 2025•0 citations
Accurate carbon-emissions prediction is critical for evidence-based climate policy, where even modest improvements can influence emission–reduction targets. However, high-dimensional datasets often exhibit noise, nonlinear dependencies, and multicollinearity that degrade model stability. We propose QMICO-PCA, a hybrid dimensionality-reduction method that integrates quantum annealing (QA)-based feature selection with principal component analysis (PCA). In the selection stage, mutual information (MI) and Pearson correlation (CO) are combined multiplicatively within a QUBO formulation to jointly reward nonlinear relevance and penalize linear redundancy, enabling balanced subset optimization. The selected features are then transformed via PCA to enhance generalization and mitigate overfitting. We evaluate QMICO-PCA against classical approaches (RFE, LASSO, ELN), with and without PCA, using three regression models on the Our World in Data carbon dataset (13,706 records; 79 features; 1970–2023). Experiments are conducted on heterogeneous QA-based solvers to assess optimization robustness. QMICO-PCA achieves the strongest overall performance (average RMSE = 1134.12, MAE = 237.54, R2R^2R2 = 0.8020), corresponding to a 4-7% error reduction over classical methods. In multi-year emission prediction, such reductions can lead to lower cumulative projection errors, supporting more reliable quantitative policy design. Statistical analysis confirms significance (Friedman p=0.0018p=0.0018p=0.0018 ) with a strong effect size ( W=0.975W=0.975W=0.975 ) and significant Holm-adjusted post-hoc comparisons ( pholm<0.05p_{\text {holm}}<0.05pholm​<0.05 ). As additional validation, the proposed method is further evaluated on small-scale carbon-emissions datasets, where it also demonstrates consistent performance. Overall, these findings suggest that the proposed method provides a competitive approach for high-dimensional carbon-emissions modeling.
As global carbon emissions continue to rise, achieving carbon neutrality has become a primary objective worldwide. Predictive modeling for carbon emissions has emerged as a crucial strategy, with numerous methods developed to forecast and manage emissions effectively. Traditional models, which predominantly employ classical computing-based metaheuristic optimization techniques, often face limitations in reaching global optima, impacting prediction accuracy. This study introduces a hybrid quantum-classical approach to enhance prediction models, particularly through feature dimensionality reduction, which is essential for improving model performance. Quantum algorithms, especially quantum annealing (QA), have shown advantages in addressing complex optimization problems by utilizing principles such as superposition and tunneling, thus offering significant benefits over classical methods. The integration of QA with classical dimensionality reduction techniques, such as principal component analysis (PCA), represents a novel advancement in managing high-dimensional data within predictive models. By optimizing feature selection, our approach aims to increase both the accuracy and computational efficiency of carbon neutrality models. This paper proposes hybrid quantum-classical optimization opportunities that can serve as a foundation for future studies, ultimately leading to more robust and accurate predictive models.
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.
Quantum Support Vector Machines (QSVMs) have recently emerged as a promising approach for biomedical classification tasks. However, their practical deployment remains constrained by limited qubit availability and sensitivity to high-dimensional feature spaces. This study proposes a hybrid framework integrating Pearson correlation–based feature selection and Principal Component Analysis (PCA) with QSVM for breast cancer diagnosis. First, Pearson correlation analysis is employed to remove redundant and weakly relevant features. Subsequently, PCA projects the selected attributes into a compact subspace while preserving most of the original data variance. This dimensionality reduction strategy decreases the number of qubits required for quantum encoding and improves computational efficiency. Experiments conducted on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the proposed hybrid QSVM achieves 98% classification accuracy, outperforming or matching existing classical and quantum-based approaches. The results confirm that combining classical preprocessing techniques with quantum classifiers provides a robust and resource-efficient solution for biomedical data analysis.
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning models. This work investigates the possibility of the usefulness of a quantum annealer to tackle the challenge of selecting features based on maximizing mutual information (MI) and conditional mutual information (CMI). Calculating the optimal feature set for maximum MI and CMI remains computationally intractable for large datasets on classical computers, even with approximation methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. Importantly, the study demonstrates the relevancy of this approach in identifying the best feature combinations that maximizes the MI and CMI. To showcase its real-world applicability, we apply MIQUBO to forecasting the price of used excavators. Our results demonstrate that using the MIQUBO approach there is an improvement in the prediction of machine learning models for datasets, with a smaller MI concentration on a subset of all features. • Problem Addressed: We tackle the computationally difficult problem of feature selection for machine learning models, specifically by maximizing mutual information (MI) and conditional mutual information (CMI) between features and the target variable. • Proposed Solution: We employ the adiabatic quantum computation paradigm to tackle this problem. The study utilizes a hybrid annealing approach, specifically the D-Wave Kerberos framework, which combines classical and quantum algorithms to solve the complex optimization problem. The feature selection task is reformulated as a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) problem, which can be solved on a quantum annealer. • Real-World Application: The MIQUBO method is applied to a practical, real-world scenario: forecasting the prices of used excavators. • Key Findings: The research demonstrates that using the MIQUBO approach leads to an improvement in the prediction performance of machine learning models. This is particularly true for datasets where the mutual information is less concentrated on a small subset of features.
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.
Quantum neural networks (QNNs) offer a promising avenue for machine learning by leveraging quantum computation, but practical QNN models are constrained by the low dimensionality of the input data they can handle. This paper proposes a hybrid quantum-classical neural network architecture that incorporates principal component analysis (PCA) for dimensionality reduction and variational quantum circuits (VQC) for quantum feature encoding. Focusing on the Wisconsin Diagnostic Breast Cancer dataset (569 instances, 30 features) as a representative biomedical classification task, in this work, we design hybrid quantum-classical neural network (QNN) models using 5-qubit and 10-qubit quantum circuits. For comparison, we use classical baseline identical to the downstream head but without the VQC+measurement. The preprocessed inputs feed the classifier head directly; only the input layer dimension is adjusted to the number of features. The 5\mathbf{5}5-qubit hybrid model is evaluated under two conditions: with and without principal component analysis (PCA) applied as a preprocessing step to investigate the effect of dimensionality reduction on model performance. In contrast, the 10 -qubit hybrid model is evaluated without PCA, directly processing the input features to explore its capacity to handle higher-dimensional data within the quantum circuit. Performance is measured in terms of classification accuracy, precision, recall, and F1-score. We evaluate the model’s robustness by injecting noise into the training data, finding that the PCA-enhanced hybrid QNN maintains competitive accuracy and resilience under perturbation. This study highlights the potential of PCA-driven variational quantum encodings to enhance QNNs for reliable biomedical data classification.