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International Conference on Pervasive Computing and Social Networking (ICPCSN)

Analysis of Quantum SVM and KNN on Azure Quantum

Savita Kumari Sheoran, Vikesh Yadav, Rakesh Kumar Sheoran

Published May 14, 2025• 0 citations
Publisher

Abstract

Integrating quantum computing with machine learning proposes promising advancement by harnessing quantum parallelism and entanglement to enhance computational efficiency. This study investigates the implementation of a quantum support vector machine and quantum k nearest neighbor using azure quantum Ionq and Quantinuum simulators, utilizing Z, ZZ and Pauli feature maps to transform traditional data into quantum states, enabling the quantum enhanced learning. Four datasets are being carried out for this experiment: iris, wine, Indian liver patient and Rain dataset. To assess classification effectiveness, model performance is assessed using accuracy, F1 score, precision and recall. The findings thoroughly examine the influence of quantum simulators on quantum model performance, emphasizing the compromises of computational feasibility. This research contributes to the ongoing development of quantum machine learning by evaluating its practical applicability and current limitations.

DOI
10.1109/icpcsn65854.2025.11035214

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Integrating quantum computing with machine learning proposes promising advancement by harnessing quantum parallelism and entanglement to enhance computational efficiency. This study investigates the implementation of a quantum support vector machine and quantum k nearest neighbor using azure quantum Ionq and Quantinuum simulators, utilizing Z, ZZ and Pauli feature maps to transform traditional data into quantum states, enabling the quantum enhanced learning. Four datasets are being carried out for this experiment: iris, wine, Indian liver patient and Rain dataset. To assess classification effectiveness, model performance is assessed using accuracy, F1 score, precision and recall. The findings thoroughly examine the influence of quantum simulators on quantum model performance, emphasizing the compromises of computational feasibility. This research contributes to the ongoing development of quantum machine learning by evaluating its practical applicability and current limitations.
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In recent years, quantum machine learning has emerged as a promising intersection between quantum physics and artificial intelligence, particularly in domains requiring advanced pattern recognition such as healthcare. This study investigates the effectiveness of Quantum Support Vector Machines (QSVM), which leverage quantum mechanical phenomena like superposition and entanglement to construct high-dimensional Hilbert spaces for data classification. Focusing on lung cancer diagnosis, a concrete and critical healthcare application, we analyze how different quantum feature maps influence classification performance. Using a real-world dataset of 309 patient records with significant class imbalance (39 non-cancer vs. 270 cancer cases), we constructed six balanced subsets for robust evaluation. QSVM models were implemented using Qiskit and executed on the qasm simulator, employing three distinct quantum feature maps: ZFeatureMap, ZZFeatureMap, and PauliFeatureMap. Performance was assessed using accuracy, precision, recall, specificity, and F1-score. Results show that the PauliFeatureMap consistently outperformed the others, achieving perfect classification in three subsets and strong performance overall. These findings demonstrate how quantum computational principles can be harnessed to enhance diagnostic capabilities, reinforcing the importance of physics-based modeling in emerging AI applications within healthcare.
The k-nearest neighbor algorithm (k-NN) is a straightforward and highly effective classical machine learning method that categorises unidentified test instances by identifying the k-nearest Neighbors from the collection of m-training instances. It is widely acknowledged that integrating machine learning algorithms with quantum computing offers tremendous benefits in some applications involving higher dimensional datasets. This research examines the quantum k-nearest neighbor (QK-NN) algorithm executed within the PennyLane’s default. qubit framework. The paper assesses the benefits of incorporating quantum computing into machine learning problems by focusing on quantum circuits and quantum feature maps for quantum-enhanced classifiers. The PennyLane framework is employed to model the behaviour on four datasets viz. Iris, Wine, Rain, Indian Lever. The implementation results reveal that quantum-enhanced k-NN Combined feature maps exhibit superior performance on most of the Accuracy, Precision, Recall and F-1 score parameters compared to individual feature maps.
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.
Quantum-assisted models can surpass classical counterparts in specific domains by leveraging quantum superposition entanglement for more efficient data representation. This study investigates a Quantum neural network framework executed on various Quantum simulators, including ionq, Cirq, Pennylane and Quantinnum backends. The iris, wine, Indian liver patient and rain datasets are employed as benchmarking datasets to assess the efficacy of quantum-based classification models. The QNN utilises parameterized quantum circuits and quantum feature maps to learn complex patterns in data, optimizing classification decision boundaries through quantum optimization techniques. The study evaluates QNN performance across different quantum hardware platforms, assessing key metrics such as accuracy, recall, F1 score, and precision. Preliminary results indicate that QNN-based classification effectively captures feature dependencies through quantum correlations, offering competitive performance in data classification tasks. These findings contribute to the broader exploration of quantum-enhanced classification and the potential of quantum devices in real-world data analysis.