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arXiv:2310.10910

Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines

Davut Emre Tasar, Kutan Koruyan, Ceren Öcal Taşar

Published Oct 17, 2023• 0 citations
arXivHTMLPDF

Abstract

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0

DOI
10.48550/arxiv.2310.10910

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This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0
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.
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Quantum Machine Learning is an active research field where the power of quantum mechanics is integrated into machine learning algorithms to solve problems that are intractable for classical computers. Quantum support vector machine (QSVM) was one of the first quantum algorithms that got implemented on real quantum hardware. It demonstrated promising outcomes as compared to classical Support vector machine (SVM) through many implementations. However, it encounters challenges in scaling with large datasets and in resource effectiveness when compare to the usage of innovative classical algorithms in the Noisy Intermediate Scale Quantum era. Here is a review of some latest developments of QSVM and discussion on prospects of QSVM application in the future.
Quantum Machine Learning (QML) integrates principles of quantum computation with statistical learning to address classification tasks. This paper conducts a comparative study of three paradigms—classical Support Vector Classification (SVC), Quantum Support Vector Classification (QSVC) using quantum kernels, and QSVC enhanced via the Pegasos stochastic algorithm. We benchmark classification accuracy on a SARS-CoV-2 dataset, analyzing model robustness and resource requirements. Our results indicate that while classical SVC maintains superior scalability and stability for large datasets, QSVC variants show competitive accuracy across simulator backends (Central Processing Units or CPUs and Graphical Processor Units or GPUs) and may achieve quantum advantage as hardware matures. Implications for hybrid workflows and future research directions are discussed.
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Quantum-enhanced techniques are widely employed to address machine learning difficulties. This work compares Support Vector Classifier (SVC) with Variational Quantum Classifier (VQC) on the Iris dataset, a typical machine learning benchmark. This study compares the performance, accuracy, and efficiency of two approaches: classical computing and quantum computing. The paper emphasizes the positive and negative aspects of each method, as well as practical data demonstrating quantum computing's potential for bettering machine learning tasks. This comparison offers helpful perspectives into the practical applications of quantum algorithms, providing an enhanced awareness of quantum machine learning's abilities and possibilities.