Ensemble Learning, a technique that combines multiple base models to enhance performance and reduce variance, is a well-established approach in machine learning. In recent years, quantum machine learning has emerged as a field aiming to accelerate classical machine learning through quantum algorithms. In this paper, we introduce a quantum ensemble learning framework, with a particular focus on utilizing the parallel amplitude amplification (parallel AA) technique when employing a quantum support vector machine (QSVM) as the base learner. This approach reduces complexity compared to classical ensemble techniques, making it a promising advancement in the field of quantum machine learning. It demonstrates significant improvements in time complexity and maintains competitive accuracy when compared to classical bagging, including support vector machine (SVM) and least squares support vector machine (LS-SVM).
Ensemble Learning, a technique that combines multiple base models to enhance performance and reduce variance, is a well-established approach in machine learning. In recent years, quantum machine learning has emerged as a field aiming to accelerate classical machine learning through quantum algorithms. In this paper, we introduce a quantum ensemble learning framework, with a particular focus on utilizing the parallel amplitude amplification (parallel AA) technique when employing a quantum support vector machine (QSVM) as the base learner. This approach reduces complexity compared to classical ensemble techniques, making it a promising advancement in the field of quantum machine learning. It demonstrates significant improvements in time complexity and maintains competitive accuracy when compared to classical bagging, including support vector machine (SVM) and least squares support vector machine (LS-SVM).
ABSTRACT Ensemble learning plays a central role in improving the generalization of machine learning models, yet its full potential remains underexplored in the quantum domain. In this work, we propose a Quantum Ensemble Learning (QEL) algorithm that leverages the parallelism of quantum computing to construct diverse classifiers in a shallow‐circuit ensemble. The method uses a control‐qubit‐based sampling strategy to create superpositions of training subsamples, enabling simultaneous evaluation of multiple weak learners. The algorithm proceeds through four stages: quantum state preparation, superposition‐based sampling, learning via quantum interference, and final measurement. Our framework is flexible and allows different quantum classifiers to be used as base learners. We demonstrate this by implementing a robust QSVM‐based ensemble with quantum feature extraction using PauliFeatureMaps. Unlike classical methods, this technique adds classifiers to time complexity additively rather than multiplicatively due to the parallelism of quantum computing. To demonstrate the workflow of the proposed algorithm, we include a step‐by‐step example using the XOR problem. Simulation results further validate the effectiveness of the QEL framework—enhanced by the integration of a Quantum Support Vector Machine (QSVM) classifier with quantum feature extraction—showing improved classification accuracy compared to existing algorithms across several UCI benchmark datasets using IBM Qiskit.
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.
Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.
Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks. Through a comparative analysis, we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality. As a case study, we explored the potential of quantum paradigms in the context of a real-world problem: classifying pancreatic cancer biomarker data. The Support Vector Classifier (SVC) algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework. The classical approach as well as the quantum-based techniques reported similar accuracy. This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset. Remarkably, quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency. This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.
Quantum computing has demonstrated remarkable prowess in addressing factorization issues and unordered search problems, showcasing its quantum parallelism capabilities that enable exponential speed-up for specific computational challenges. However, the seamless integration of classical and quantum computing to harness accelerated computation speed presents unique challenges. This paper delves into the intricacies of this integration, focusing on the current state of quantum machine learning (QML) and evaluating the performance of classical and quantum algorithms in terms of accuracy. To address these challenges, we conducted experiments utilizing three datasets for binary classification, implementing both classical Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our investigations reveal that the QSVM algorithm consistently outperforms its classical counterpart on complex datasets. Notably, the performance gap between quantum and classical models widens with increasing dataset complexity, shedding light on the susceptibility of simple classical models to overfitting when confronted with intricate datasets.Despite the ongoing development required for quantum hardware with ample resources, our findings underscore the significant potential of quantum machine learning, particularly in unsupervised learning and generative models. As we move forward, it is imperative to channel more efforts into exploring novel quantum learning models capable of harnessing the inherent power of quantum mechanics to overcome the constraints of classical machine learning. This research contributes to the ongoing discourse on the future of quantum computing, emphasizing the need for continued exploration and innovation in quantum machine learning methodologies.