Quantum Machine Learning is a growing field with the potential to unlock several opportunities in groundwater management. A novel approach integrating advanced quantum machine learning (QML) models with classical machine learning (ML) algorithms for robust groundwater quality assessment, using the SABHQ (Systematically Averaged Battery Hybrid Quantum) method, is proposed. We attempted to reduce noise and enhance generalization by stimulating composite sampling across both ML and QML pipelines. Accuracy, precision, recall, and F 1 scores were calculated across all models, using normalization and aggregation strategies to achieve relatively unbiased benchmarking. We observed that the dominant predictors, such as EC, Cl, HCO 3 , SO 4 , Na, TH, Latitude, and Longitude, reflect both spatial variability and anthropogenic influences on groundwater quality. The systematic averaging method yielded noticeable improvements for QML models, with QCNN achieving accuracy and F 1 scores of up to 0.95 and 0.98, respectively, outperforming classical machine learning models. We report that QCNN metrics improved due to aggregation, noise reduction, and possibly quantum entanglement effects, leading to better generalization, whereas VQC showed gradual but minor improvements. We believe our work can be extended to other environmental domains, not only to refine the method we proposed but also to enhance its adaptability.
Quantum Machine Learning is a growing field with the potential to unlock several opportunities in groundwater management. A novel approach integrating advanced quantum machine learning (QML) models with classical machine learning (ML) algorithms for robust groundwater quality assessment, using the SABHQ (Systematically Averaged Battery Hybrid Quantum) method, is proposed. We attempted to reduce noise and enhance generalization by stimulating composite sampling across both ML and QML pipelines. Accuracy, precision, recall, and F 1 scores were calculated across all models, using normalization and aggregation strategies to achieve relatively unbiased benchmarking. We observed that the dominant predictors, such as EC, Cl, HCO 3 , SO 4 , Na, TH, Latitude, and Longitude, reflect both spatial variability and anthropogenic influences on groundwater quality. The systematic averaging method yielded noticeable improvements for QML models, with QCNN achieving accuracy and F 1 scores of up to 0.95 and 0.98, respectively, outperforming classical machine learning models. We report that QCNN metrics improved due to aggregation, noise reduction, and possibly quantum entanglement effects, leading to better generalization, whereas VQC showed gradual but minor improvements. We believe our work can be extended to other environmental domains, not only to refine the method we proposed but also to enhance its adaptability.
Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with 97.76% accuracy, 97.92% precision, 97.76% recall, and an F 1 -score of 97.77%, outperforming classical baselines by up to 4%. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.
Abstract Our study proposes the MDQ-3L framework, a multi-metric, quantum-aware hierarchy for benchmarking classical machine learning, deep learning, and quantum machine learning models in air quality prediction. PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene were considered predictors of AQI. Seventeen ML regressors, nine DL models, and seven QML circuits were evaluated using MAE, MSE, RMSE, R², RMSLE, MAPE, and QML-specific expressibility (0.739-2.0) and entangling power (0-0.398). Aggregated via min-max normalization and weights, the framework penalizes negative R² and high structural costs, yielding CatBoost and DBN as top performers, while QML trailed by 25–40% in composite scores due to generalization gaps and barren plateau risks. MDQ-3L aligns circuits to targets, reducing QCNN/TTN penalties by ~ 30% relative to non-benchmarked rankings. Our approach exposes QML limitations under NISQ constraints (4–6 qubits, shallow depth), supporting low-entanglement ansätze like SELR/HEA for 10–15% potential gains, and provides an interpretable tool for hybrid environmental modeling, advancing Quantum Machine Intelligence benchmarks.
Quantum machine learning has emerged as a promising approach to enhance computational efficiency by leveraging the principles of quantum computing. However, the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, such as noise, limited qubit availability, and circuit depth constraints, restrict the implementation of fully quantum models. To address these challenges, this study focuses on the design, implementation, and performance evaluation of hybrid quantum-classical machine learning (HQML) models. The proposed approach integrates parameterized quantum circuits with classical optimization techniques to enable efficient learning within NISQ environments. The study employs standard benchmark datasets, including Iris, Breast Cancer, and MNIST, to evaluate the performance of the hybrid model. The results indicate that the HQML model achieves competitive accuracy on small and medium-sized datasets while maintaining balanced precision, recall, and F1-score. However, performance declines for complex datasets due to hardware limitations and noise effects. Additionally, the hybrid model demonstrates a lower number of parameters compared to classical deep learning models but requires higher training time due to iterative quantum-classical optimization. The findings highlight that hybrid quantum-classical models provide a practical and scalable approach for utilizing quantum computing in the current technological landscape. Although challenges related to noise, scalability, and computational overhead persist, advancements in quantum hardware and algorithm design are expected to improve performance. This study contributes to the growing field of quantum machine learning by providing a systematic framework for evaluating hybrid models on NISQ devices and identifying key areas for future research.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by changing basic input features into the best mixed forms using classical weight matrices and quantum state encoding. A unitary operator refines quantum states to enhance feature extraction and representation. Gradient-based alterations repeatedly modify feature weights during optimization to improve accuracy. Quantum circuits not only change data but also enhance system separability and resilience. Normalizing and enhancing extracted properties using quantum and classical transformations ensures stability and consistency. Performance trials demonstrate that the hybrid technique improves accuracy, precision, recall, computational complexity, execution time, and energy economy. The hybrid model always outperforms machine learning, deep learning, and quantum mechanics-based approaches. The technique is now more scalable, making enormous data sets easier to analyze. Faster convergence rates and noise tolerance improve model performance in real-world applications. The findings suggest that quantum advances and classical learning approaches improve model interpretability and decision-making reliability. The research found hybrid quantum-classical machine learning to be a powerful, scalable, and efficient predictive modeling solution. This method improves precision while conserving computing power. By using both paradigms, the technique provides deeper insights and better predictions. As a major facilitator for future AI-driven applications, the hybrid model provides a possible solution for difficult machine learning challenges. These developments make hybrid models available.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.