This paper introduces a Bayesian Optimization-based Multiple Kernel Quantum Support Vector Machine (BO-MKQSVM) to overcome the limitations of traditional Quantum Support Vector Machines (QSVMs), where a single kernel's representational power is insufficient, and multi-kernel weight optimization is complex. By integrating multiple quantum feature maps and leveraging Bayesian Optimization, the method automatically tunes kernel weights and regularization parameters, thereby enhancing model performance and generalization ability in small-sample classification. Experiments on publicly available datasets, including Iris, Fertility, and Immunotherapy, show that BO-MKQSVM outperforms classical models such as SVM, Random Forest, Gradient Boosting Decision Trees, and Multilayer Perceptrons in accuracy, recall, and F1-score, demonstrating superior classification performance and robustness.
This paper introduces a Bayesian Optimization-based Multiple Kernel Quantum Support Vector Machine (BO-MKQSVM) to overcome the limitations of traditional Quantum Support Vector Machines (QSVMs), where a single kernel's representational power is insufficient, and multi-kernel weight optimization is complex. By integrating multiple quantum feature maps and leveraging Bayesian Optimization, the method automatically tunes kernel weights and regularization parameters, thereby enhancing model performance and generalization ability in small-sample classification. Experiments on publicly available datasets, including Iris, Fertility, and Immunotherapy, show that BO-MKQSVM outperforms classical models such as SVM, Random Forest, Gradient Boosting Decision Trees, and Multilayer Perceptrons in accuracy, recall, and F1-score, demonstrating superior classification performance and robustness.
Quantum machine learning combines the power of quantum computing with machine learning to address complex computational challenges. In this work, we propose an integrated approach that leverages Quantum Non-Negative Matrix Factorization (QNMF) for feature selection and dimensionality reduction, coupled with Quantum Support Vector Machines (QSVM) for accurate classification. By combining the strengths of these two quantum techniques, our approach reduces data complexity while maintaining classification accuracy. We evaluate the performance of our method across multiple datasets from finance, healthcare, and e-commerce. Experimental results demonstrate that the proposed QNMF+QSVM approach achieves superior accuracy compared to classical machine learning methods such as SVM, Random Forests, and Decision Trees, highlighting its potential for solving real-world classification problems.
With the rapid growth of data volume and complexity, traditional machine learning algorithms encounter significant challenges in handling large-scale and high-dimensional datasets. To address these limitations, this paper proposes an enhanced classification framework based on Quantum Resonance Dimensionality Reduction (QRDR) and Hybrid Quantum Ensemble Learning (HQEL). First, a Multi-Kernel Quantum Support Vector Machine (MKQSVM) is introduced, which integrates the advantages of multiple quantum kernel functions and employs Bayesian optimization to automatically determine the optimal kernel combination. This approach enhances the model’s adaptability and generalization ability on heterogeneous data. Second, the hybrid quantum ensemble learning model combines MKQSVM and Quantum Convolutional Neural Network (QCNN) through a stacking ensemble strategy with a classical meta-learner, thereby achieving improved prediction accuracy and stability. Moreover, the proposed QRDR algorithm is utilized to reduce the dimensionality of high-dimensional data while retaining critical features and minimizing redundancy, which effectively improves both training efficiency and predictive performance of the quantum models. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches on multiple benchmark datasets, particularly in classifying complex and high-dimensional data.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.
Quantum machine learning has emerged as a promising paradigm that leverages quantum computational principles to potentially outperform classical approaches on specific learning tasks. This paper presents a comprehensive empirical investigation comparing Quantum Machine Learning (QML), specifically Quantum Support Vector Machines (QSVM) against Classical Machine Learning (CML) baselines including Logistic Regression, K Nearest Neighbors and classical SVMs. We implement quantum kernel methods using variational quantum circuits with angle embedding, IQP and ZZ feature maps on datasets exhibiting varying structural complexity. Our experiments on synthetic quantum generated datasets (Quantum Parity, Quantum Entanglement, Quantum Interference) demonstrate that QSVM achieves superior classification accuracy with improvements ranging from 2.9 % to 60 % over the best classical baselines achieving a 55.6 % overall win rate. Notably, on the Quantum Parity dataset, QSVM consistently outperformed all classical models across multiple configurations. On real-world benchmark datasets (Breast Cancer, Iris, Wine), QSVM Angle achieved 88% accuracy on Breast Cancer compared to 76% for classical SVM Linear a 15.8 % relative improvement while matching the best classical performance on Iris(100%)and Wine(92%). All experiments were conducted using quantum circuit simulation on classical hardware. These results provide empirical evidence that quantum kernel methods offer meaningful advantages over classical approaches on datasets with complex feature interactions. We discuss the theoretical foundations of these improvements including quantum feature space expressivity and kernel alignment.
Background Quantum support vector machines and quantum kernel methods have emerged as promising approaches within quantum machine learning, with the goal of leveraging quantum computing to enhance classification performance and computational efficiency. This review systematically surveys recent advances in QSVM and kernel‐based quantum classifiers, and analyzes their algorithmic frameworks, experimental implementations, and practical challenges. Methods We systematically examine QSVM approaches, including three schemes based on HHL algorithm, Hadamard test, and variational optimization, alongside multi‐class extension and quantum feature mapping. Result Findings indicate that QSVM can offer theoretical speed‐ups in specific settings, particularly when combined with quantum feature mappings that encode data into high‐dimensional Hilbert spaces. However, current implementations remain constrained by hardware limitations, and a lack of large‐scale general validation. Most studies focus on proof‐of‐principle experiments with limited real‐world applicability. Conclusion While promising, QSVM requires further work on scalability, noise resilience, and real‐world integration. Future research should focus on robust algorithms and empirical studies.