This research evaluates the performance of classical and quantum machine learning models for epileptic seizure detection using the BONN and DELHI EEG datasets. The data pipeline includes a preprocessing step with feature extraction followed by multiple dimensionality reduction techniques. Seven quantum data encoding strategies (Phase, Angle, Dense Angle, Pauli Feature Map, Efficient SU2, Z Feature Map, and ZZ Feature Map) were evaluated to analyze their impact on the expressivity of quantum circuits and model performance. Classical models, including Decision Tree and Random Forest classifiers, consistently achieved high accuracy on the BONN and Delhi datasets, with performance exceeding 90\% across most dimensionality-reduction settings. Quantum models demonstrated competitive yet generally lower performance than classical baselines; however, certain combinations of encoding methods and dimensionality reduction techniques achieved the most promising results for both datasets. Overall, the study highlights the influence of encoding strategies and dataset characteristics on quantum machine learning performance and provides a comparative analysis that supports future optimization of quantum approaches for EEG-based seizure detection.
This research evaluates the performance of classical and quantum machine learning models for epileptic seizure detection using the BONN and DELHI EEG datasets. The data pipeline includes a preprocessing step with feature extraction followed by multiple dimensionality reduction techniques. Seven quantum data encoding strategies (Phase, Angle, Dense Angle, Pauli Feature Map, Efficient SU2, Z Feature Map, and ZZ Feature Map) were evaluated to analyze their impact on the expressivity of quantum circuits and model performance. Classical models, including Decision Tree and Random Forest classifiers, consistently achieved high accuracy on the BONN and Delhi datasets, with performance exceeding 90% across most dimensionality-reduction settings. Quantum models demonstrated competitive yet generally lower performance than classical baselines; however, certain combinations of encoding methods and dimensionality reduction techniques achieved the most promising results for both datasets. Overall, the study highlights the influence of encoding strategies and dataset characteristics on quantum machine learning performance and provides a comparative analysis that supports future optimization of quantum approaches for EEG-based seizure detection.
The early detection and accurate classification of diabetes health indicators are crucial for effective disease management and prevention. This study aims to compare the performance of classical and quantum machine learning models in feature selection and classification on the Diabetes Health Indicators dataset. Initially, classical machine learning methods were employed to preprocess the data, including normalization and scaling, followed by feature selection using Lasso regression. Various traditional models, such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes, were evaluated. Among these, the Logistic Regression model achieved the highest accuracy at 85%, while other models also demonstrated competitive performance with accuracies ranging from 82% to 85%. Subsequently, quantum machine learning techniques were applied using the selected features to assess their effectiveness. Quantum circuits were created using Cirq, and parameter optimization was performed through Quantum Feature Mapping and Quantum Feature Transformation. The Quantum Support Vector Machine (QSVM) model attained an accuracy of 84.33%, showing potential for matching the performance of traditional models. The results suggest that quantum machine learning models can offer comparable accuracy to classical methods in the classification of diabetes health indicators. This study highlights the potential benefits of integrating quantum techniques in complex data processing and recommends further exploration in future research to fully harness the capabilities of quantum machine learning.
Automated epileptic seizure detection from electroencephalogram (EEG) recordings remains a challenging biomedical signal analysis task because of the nonlinear, non-stationary, and high-dimensional characteristics of neural activity. Although deep learning has substantially improved automated seizure classification, conventional architectures may exhibit limited capability in modeling complex latent feature interactions while maintaining computational efficiency in biomedical applications. This study presents an entanglement-driven hybrid quantum–classical representation learning framework that integrates Continuous Wavelet Transform (CWT)-based time–frequency analysis, EfficientNet-B0 feature extraction, quantum-compatible latent feature compression, angle-based quantum state encoding, and a customized four-qubit Variational Quantum Convolutional Neural Network (QCNN) for EEG seizure classification. The proposed shallow variational quantum architecture is designed to facilitate expressive quantum feature learning while remaining compatible with the computational constraints of near-term noisy intermediate-scale quantum (NISQ) systems. An experimental evaluation was conducted on a balanced subset of 4,000 EEG segments from the UCI Epileptic Seizure Recognition Dataset. Stratified five-fold cross-validation achieved a mean classification accuracy of 97.80% ± 1.50%, demonstrating consistent predictive performance and generalization across several data partitions. Furthermore, evaluation on an independent held-out test set yielded 99.50% accuracy, 99.03% precision, 100.00% recall, 99.51% F1-score, and an area under the ROC curve (AUC) of 1.000. To comprehensively assess the proposed framework, additional investigations, including quantum circuit expressibility, barren plateau analysis, optimization trainability, latent feature-space separability, robustness evaluation, computational scalability, and patch-level quantum attention visualization, were performed, offering complementary evidence regarding representation quality, optimization stability, and model interpretability. Although the proposed framework is currently validated using classical quantum simulation, the results demonstrate the feasibility of hybrid quantum–classical representation learning for intelligent EEG seizure analysis and establish a foundation for future deployment on emerging quantum-computing platforms.
In this study, the performance of classical and quantum machine learning models was compared using the Breast Cancer dataset, which consists of diagnostic data aimed at classifying breast tumor types. Breast cancer, being one of the most common and life-threatening cancers in women, requires accurate diagnostic tools for early detection and effective treatment. The primary objective of this study is to evaluate the accuracy of quantum-assisted models through quantum feature selection methods. Initially, classical machine learning algorithms such as Support Vector Machines (SVM), Decision Trees, Random Forests, and Logistic Regression were applied to the dataset for baseline analysis. Subsequently, a quantum feature map was constructed using the Cirq library, enabling feature transformation based on this map. The classification was performed using the SVM model with the quantum-transformed features. The Logistic Regression and SVM models demonstrated the highest performance among classical machine learning models, achieving an accuracy rate of 96.49%, followed by Random Forest at 94.74% and Decision Tree at 92.11%. In the context of quantum feature transformation, the model utilizing the top five selected features achieved an accuracy rate of 94.74%, in contrast to 98.25% for the model trained with all features. These findings underscore the potential of quantum feature maps in enhancing model performance compared to classical techniques. The results suggest that quantum computing may offer significant advantages when integrated into machine learning frameworks, particularly in domains such as medical diagnostics, where high accuracy is crucial.
The way in which classical data are encoded into quantum states plays a significant role in both classification performance and quantum circuit complexity in Quantum Machine Learning. In this study, the effects of different quantum feature encoding strategies on Quantum Support Vector Machine performance were investigated using five binary classification datasets. In particular, the statistical relationships between features were incorporated into quantum circuits through RY(θ) and controlled-RY(θ) gates, and this approach was compared with conventional quantum feature maps. The results demonstrate that incorporating statistical relationships into the encoding process can influence classification performance. However, more complex and densely entangled circuits do not necessarily yield higher performance. In addition, a composite evaluation metric was employed to jointly assess predictive performance, generalization, and circuit cost. The findings across the five datasets indicate that the choice of quantum feature encoding strategy should account for the underlying structure of the data and that predictive performance should be evaluated together with quantum circuit complexity.
This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.
When it comes to realizing immediate quantum advantages in practical applications like machine learning and optimization techniques, numerous methods have emerged as a frontrunner technique. Such algorithms often involve quantum circuits for data encoding and the training of Quantum Neural Networks (QNNs) to minimize target functions when applied to tasks involving classical data. Quantum Machine Learning (QML) is a promising area of quantum computing because it may effectively handle difficult learning tasks by taking advantage of the high dimensional Hilbert space to train better representations from limited data. Despite QML's rising popularity, few academic works have addressed the language's security features. There has been a lot of buzz about the promising new discipline of quantum machine learning. Training a parameterized quantum circuit is a common practice in contemporary QML for performing data analysis on classical or quantum datasets. To make accurate predictions on an independent test dataset, state-of-the-art QML techniques first variationally optimize a parameterized quantum circuit using the training dataset. Quantum modeling in QML has also been shown to offer an exponential benefit in sample complexity. In the realms of pattern recognition, machine learning, and data mining, feature dimensionality reduction as a crucial link in the process has become one hot and tough spot. Most academics have gravitated towards this topic because of the difficulty of its studies. The goal of this research is to learn how to implement low loss in the procedure of feature dimension reduction, preserve the integrity of the original data, locate the most effective mapping, and obtain the best possible low-dimensional data. Preliminary findings suggest that QML models may offer advantages over classical models for classical data analysis. In this paper, a thorough investigation into generalization performance in QML is performed. This paper presents a brief survey on Qubits Encoding using Quantum Machine Learning models that help researchers to identify the limitations of the existing models and to design new efficient models for better performance levels.