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Journal articles on the topic 'Quantum support vector machine'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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Akrom, Muhamad. "Quantum Support Vector Machine for Classification Task: A Review." Journal of Multiscale Materials Informatics 1, no. 2 (2024): 1–8. http://dx.doi.org/10.62411/jimat.v1i2.10965.

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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.

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Orazi, Filippo, Simone Gasperini, Stefano Lodi, and Claudio Sartori. "Hybrid Quantum Technologies for Quantum Support Vector Machines." Information 15, no. 2 (2024): 72. http://dx.doi.org/10.3390/info15020072.

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Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.

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Zhang, Rui, Jian Wang, Nan Jiang, and Zichen Wang. "Quantum support vector machine without iteration." Information Sciences 635 (July 2023): 25–41. http://dx.doi.org/10.1016/j.ins.2023.03.106.

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Yin, Tianzhu. "Quantum support vector machines: theory and applications." Theoretical and Natural Science 51, no. 1 (2024): 34–42. http://dx.doi.org/10.54254/2753-8818/51/2024ch0158.

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Abstract. Quantum Support Vector Machines (QSVMs) combine the fundamental principles of quantum computing and classical Support Vector Machines (SVMs) to improve machine learning performance. In this paper, the author will further explore QSVM. Firstly, introduce the basics of classical SVM, including hyperplane, margin, support vector, and kernel methods. Then, introduce the basic theories of quantum computing, including quantum bits, entanglement, quantum states, superposition, and some related quantum algorithms. Focuses on the concept of QSVM, quantum kernel methods, and how SVM runs on a quantum computer. Key topics include quantum state preparation, measurement, and output interpretation. Theoretical advantages of QSVMs, such as faster computation speed, stronger performance to process high-dimensional data, and kernal computation. In addition, the author discussed the implementation of QSVM, quantum algorithms, quantum gradient descent, and optimization techniques for SVM training. The article also discusses practical issues such as error mitigation and quantum hardware requirements. The purpose of this paper is to show the advantages of QSVM by comparing SVM and QSVM.

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Jianming Yi, Kalyani Suresh, Ali Moghiseh, and Norbert Wehn. "Variational Quantum Linear Solver Enhanced Quantum Support Vector Machine." Advances in Artificial Intelligence and Machine Learning 04, no. 02 (2024): 2164–87. http://dx.doi.org/10.54364/aaiml.2024.42124.

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Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a Least Squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, we exploit both classical and quantum computing for various subroutines of our algorithm, and effectively mitigate challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.

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Kutan, Koruyan, Öcal Coşar Ceren, and Emre Taşar Davut. "Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines." Journal of Quantum Technologies and Informatics Research 1, no. 1 (2023): 65–72. https://doi.org/10.5281/zenodo.10260090.

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Bu çalışma, klasik ve kuantum hesaplama paradigmalarındaki makine öğrenimi algoritmalarının performansı-nı karşılaştırmayı amaçlamaktadır. Özellikle, Destek Vektör Makineleri (SVM) üzerinde durarak, klasik SVM ilekuantum donanımı üzerinde çalıştırılan Kuantum Destek Vektör Makineleri (QSVM)'nin Iris veri seti üzerindekisınıflandırma başarısını değerlendirmekteyiz. Kullanılan metodoloji, Qiskit kütüphanesi ile gerçekleştirilen kap-samlı deneyler serisini ve hiperparametre optimizasyonunu içermektedir. Elde edilen sonuçlar, belirli durumlardaQSVM'lerin klasik SVM'lerle rekabet edebilecek düzeyde doğruluk sağladığını, fakat çalışma sürelerinin şu an içindaha uzun olduğunu göstermektedir. Ayrıca, kuantum hesaplama kapasitesinin ve paralellik derecesinin arttırıl-masının, kuantum makine öğrenimi algoritmalarının performansını önemli ölçüde iyileştirebileceğini belirtmek-teyiz. Bu çalışma, kuantum çağında makine öğrenimi uygulamalarının mevcut durumu ve gelecekteki potansiyelihakkında değerli içgörüler sunmaktadır. Colab: https://t.ly/QKuz0

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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.

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Leporini, Roberto, and Davide Pastorello. "Support Vector Machines with Quantum State Discrimination." Quantum Reports 3, no. 3 (2021): 482–99. http://dx.doi.org/10.3390/quantum3030032.

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We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification.

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DEMIRTAS, F., and E. Erkan TANYILDIZI. "Hyper-parameter Tuning for Quantum Support Vector Machine." Advances in Electrical and Computer Engineering 22, no. 4 (2022): 47–54. http://dx.doi.org/10.4316/aece.2022.04006.

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Huang, Xi, Shibin Zhang, Chen Lin, and Jinyue Xia. "Quantum Fuzzy Support Vector Machine for Binary Classification." Computer Systems Science and Engineering 45, no. 3 (2023): 2783–94. http://dx.doi.org/10.32604/csse.2023.032190.

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Ruskanda, Fariska Zakhralativa, Muhammad Rifat Abiwardani, Rahmat Mulyawan, Infall Syafalni, and Harashta Tatimma Larasati. "Quantum-Enhanced Support Vector Machine for Sentiment Classification." IEEE Access 11 (2023): 87520–32. http://dx.doi.org/10.1109/access.2023.3304990.

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Du, Sunwen, and Yao Li. "A novel deformation forecasting method utilizing comprehensive observation data." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401879633. http://dx.doi.org/10.1177/1687814018796330.

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Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization–Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization–Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

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Ding, Biao, Zhaoxia Meng, Jiaheng Zou, Teng Li, and Tao Lin. "Zc(3900) observation at BESIII with QSVM method." EPJ Web of Conferences 295 (2024): 12008. http://dx.doi.org/10.1051/epjconf/202429512008.

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In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM). Meanwhile, the outcomes are compared with classical support vector machine (SVM) method. With the IBM Qiskit toolkit, the QSVM method achieves a competitive signal and background classification accuracy compared to classical methods. This study emphasizes the potential of quantum machine learning in high-energy physics research, and it reveals the feasibility of applying quantum computing in future physics data analysis.

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Rana, Anurag, Pankaj Vaidya, and Gaurav Gupta. "A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm." Materials Today: Proceedings 56 (2022): 2025–30. http://dx.doi.org/10.1016/j.matpr.2021.11.350.

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Hilmy, Nur Amalina Rahmaputri, and Muhamad Akrom. "IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT." Networking Engineering Research Operation 9, no. 2 (2024): 119–26. https://doi.org/10.21107/nero.v9i2.27781.

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Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat

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Chaharlang, Javad, Mohammad Mosleh, and Saeed Rasouli-Heikalabad. "Quantum Reversible Audio Steganalysis Using Quantum Schmidt Decomposition and Quantum Support Vector Machine." Journal of Information Security and Applications 82 (May 2024): 103755. http://dx.doi.org/10.1016/j.jisa.2024.103755.

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Windridge, David, Riccardo Mengoni, and Rajagopal Nagarajan. "Quantum error-correcting output codes." International Journal of Quantum Information 16, no. 08 (2018): 1840003. http://dx.doi.org/10.1142/s0219749918400038.

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Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.

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Gaurav, Kashyap. "Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14615549.

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At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades.

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Vashisth, Shubham, Ishika Dhall, and Garima Aggarwal. "Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis." Journal of Intelligent Systems 30, no. 1 (2021): 998–1013. http://dx.doi.org/10.1515/jisys-2020-0089.

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Abstract The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants.

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Wang, Qiwei. "Support vector machine based on the quadratic unconstrained binary optimization model." Journal of Physics: Conference Series 2858, no. 1 (2024): 012002. http://dx.doi.org/10.1088/1742-6596/2858/1/012002.

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Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

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Wei, Chunyan, and Sang-Bing Tsai. "Evaluation Model of College English Teaching Effect Based on Particle Swarm Algorithm and Support Vector Machine." Mathematical Problems in Engineering 2022 (April 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/7132900.

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Based on the principle of particle swarm algorithm and support vector machine, this article aims to improve the classification performance of college English teaching effect and explores the best support vector machine parameter optimization algorithm to promote college English teaching for the theory and application research of data analysis. First, the advantages and disadvantages of common support vector machine parameter selection methods such as grid search algorithm, gradient descent method, and swarm intelligence algorithm are studied. Secondly, this article has a detailed analysis and comparison of various other algorithms. Finally, the study analyzed the advantages and disadvantages of the quantum particle swarm algorithm, introduced the dual-center idea into the quantum particle swarm algorithm, and proposed an improved quantum particle swarm algorithm. Through simulation experiments, it is proved that the improved quantum particle swarm algorithm is more superior in optimizing the parameters of support vector machine. In general, this paper uses the PSO algorithm to simultaneously solve the SVM feature selection and parameter optimization problems and has achieved good results. Within the scope of the literature that the author has, there is still a lack of work in this area. Compared with the existing algorithms, the algorithm proposed in this paper has stronger feature selection ability and higher efficiency.

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Zhu, Xing Tong, and Bo Xu. "Power Short-Term Load Forecasting Based on QPSO-SVM." Advanced Materials Research 591-593 (November 2012): 1311–14. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1311.

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The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally, the support vector machine was used for power short-term load forecasting. In the proposed method such factors impacting loads as meteorology,weather and date types are comprehensively considered. The experimental results show that the root-mean-square relative error of the proposed method is only 1.90%, which is less than those of SVM and PSO-SVM model by 2.29% and 2.80%, respectively.

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Akrom, Muhamad. "KOMPARASI SVM KLASIK DAN KUANTUM DALAM KLASIFIKASI BINER BIJI GANDUM (SEEDS)." Networking Engineering Research Operation 9, no. 1 (2024): 49–58. https://doi.org/10.21107/nero.v9i1.28082.

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Binary classification is one of the important tasks in machine learning, with wide applications in various fields, including agriculture and food processing. This study compares the performance of the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) in wheat grain classification, focusing on accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The wheat grain dataset consists of physical features relevant to distinguish between two types of grains. The analysis results show that QSVM significantly outperforms classical SVM in all measured metrics, with higher accuracy and a better balance between precision and recall. The superiority of QSVM can be attributed to its ability to handle complex feature interactions and accelerate the training process through quantum algorithms. These findings demonstrate the potential of QSVM as a more effective model for binary classification applications. However, factors such as implementation complexity and availability of quantum computing resources need to be considered. This study provides valuable insights for the development of more efficient classification methods in the context of agriculture and other related fields.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Classification, Seeds

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Aksoy, Gamzepelin, and Zeynep Özpolat. "COMPARATIVE ANALYSIS OF CLASSICAL AND QUANTUM SVM MODELS ON MEDICAL DIAGNOSIS DATASETS." Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi 9, no. 1 (2025): 80–93. https://doi.org/10.62301/usmtd.1716034.

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Quantum-assisted machine learning approaches have become a significant area of research in the healthcare domain by offering alternative solutions to classical methods, particularly when dealing with high-dimensional and complex datasets. This study presents a comparative evaluation of the classification performance of classical Support Vector Machines (SVM) and quantum-based algorithms Quantum Support Vector Machine (QSVM) and Pegasos-QSVM on healthcare data. Experimental analyses were conducted using three distinct medical datasets related to liver disease, breast cancer, and heart failure. The results demonstrate that the QSVM model consistently achieved the highest and most stable classification accuracy. Although the Pegasos-QSVM model achieved comparable accuracy rates in certain configurations, its performance was generally more variable. Nevertheless, thanks to its lower computational cost and faster processing time, Pegasos-QSVM emerges as a promising alternative, particularly in resource-constrained environments. The findings suggest that quantum-assisted models can deliver performance levels competitive with classical approaches, particularly highlighting the effectiveness of QSVM on small- to medium-sized datasets.

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Gentinetta, Gian, Arne Thomsen, David Sutter, and Stefan Woerner. "The complexity of quantum support vector machines." Quantum 8 (January 11, 2024): 1225. http://dx.doi.org/10.22331/q-2024-01-11-1225.

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Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models corresponds to solving a convex optimization problem either via its primal or dual formulation. Due to the probabilistic nature of quantum mechanics, the training algorithms are affected by statistical uncertainty, which has a major impact on their complexity. We show that the dual problem can be solved in O(M4.67/&#x03B5;2) quantum circuit evaluations, where M denotes the size of the data set and &#x03B5; the solution accuracy compared to the ideal result from exact expectation values, which is only obtainable in theory. We prove under an empirically motivated assumption that the kernelized primal problem can alternatively be solved in O(min{M2/&#x03B5;6,1/&#x03B5;10}) evaluations by employing a generalization of a known classical algorithm called Pegasos. Accompanying empirical results demonstrate these analytical complexities to be essentially tight. In addition, we investigate a variational approximation to quantum support vector machines and show that their heuristic training achieves considerably better scaling in our experiments.

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Ajibosin, Surajudeen Shina, and Deniz Cetinkaya. "Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification." Software 3, no. 4 (2024): 498–513. http://dx.doi.org/10.3390/software3040024.

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In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.

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Kumar, Tarun, Dilip Kumar, and Gurmohan Singh. "Performance Analysis of Quantum Classifier on Benchmarking Datasets." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 375–80. http://dx.doi.org/10.37391/ijeer.100252.

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Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.

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Lin, Jie, Dan-Bo Zhang, Shuo Zhang, Tan Li, Xiang Wang, and Wan-Su Bao. "Quantum-enhanced least-square support vector machine: Simplified quantum algorithm and sparse solutions." Physics Letters A 384, no. 25 (2020): 126590. http://dx.doi.org/10.1016/j.physleta.2020.126590.

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Li, Keqin, Peng Zhao, Shuying Dai, et al. "Exploring the Impact of Quantum Computing on Machine Learning Performance." Middle East Journal of Applied Science & Technology 07, no. 02 (2024): 145–61. http://dx.doi.org/10.46431/mejast.2024.7215.

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This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.

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Lohia, Aarav. "Quantum Artificial Intelligence: Enhancing Machine Learning with Quantum Computing." Journal of Quantum Science and Technology 1, no. 2 (2024): 6–11. http://dx.doi.org/10.36676/jqst.v1.i2.9.

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Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data more efficiently than classical counterparts. These algorithms promise to accelerate tasks such as optimization, pattern recognition, and data classification, thereby advancing the capabilities of AI systems. Moreover, quantum computing offers potential breakthroughs in solving combinatorial optimization problems that are computationally intensive for classical computers. Quantum annealing and other quantum optimization techniques are explored for their application in AI, providing novel approaches to solving complex decision-making problems.

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Gugri, Apoorva. "Hate-Speech Recognition using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41180.

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The convergence of quantum computing and genomic data analysis offers a groundbreaking methodology for tackling intricate biological datasets. Traditional computational techniques often struggle with the complexities of high-dimensional genomic information. This study introduces a quantum-enhanced framework for cancer type detection, leveraging the capabilities of Quantum Support Vector Machines (QSVM). The proposed approach showcases significant advancements in efficiency, particularly in training speed and scalability, highlighting the potential of quantum algorithms as valuable extensions to classical methods. The paper also addresses the limitations of current quantum hardware and explores potential directions for future development. Keywords- Machine Learning, Transformer Models, BERT (Bidirectional Encoder Representations from Transformers), Contextual Embeddings, Offensive language Identification

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Bishwas, Arit Kumar, Ashish Mani, and Vasile Palade. "Gaussian kernel in quantum learning." International Journal of Quantum Information 18, no. 03 (2020): 2050006. http://dx.doi.org/10.1142/s0219749920500069.

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The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel is approximated to [Formula: see text] and even [Formula: see text] when [Formula: see text] and the error [Formula: see text] are small enough to be ignored, where [Formula: see text] is the dimension of the training instances, [Formula: see text] is the accuracy, [Formula: see text] is the dot product of the two quantum states, and [Formula: see text] is the Taylor remainder error term. Therefore, the run time complexity of the quantum version of the Gaussian kernel seems to be significantly faster when compared with its classical version.

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Zhang, Xiaochen, and Dongxiang Jiang. "Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine." Shock and Vibration 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9581379.

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To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA) and support vector machine (SVM) is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap), the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

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Abdulrazzaq Altarraji, Mohammed Aqeel, and Ali Mokdad. "Quantum-Assisted Machine Learning for Enhanced Fraud Detection in Cybersecurity." International Research Journal of Innovations in Engineering and Technology 08, no. 05 (2024): 319–24. http://dx.doi.org/10.47001/irjiet/2024.805042.

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Innovative methods for information security and fraud prevention are required in today's digital environment due to the expanding volume of data and the increasing complexity of cyber threats. The use of quantum computing techniques to improve fraud detection and classification systems is investigated in this study. The study's machine learning framework integrates three distinct quantum algorithms to improve classification techniques. The first technique uses a Pauli feature map and a Quantum Support Vector Classifier (QSVC) that leverages a quantum kernel to transform classical input into quantum states. The second technique use a ZZ feature map with "linear" entanglement and a support vector classifier model, utilizing quantum kernels to enhance quantum systems. The third method utilizes Variational Quantum Circuits (VQC) with actual amplitudes, which integrate quantum and conventional machine learning techniques to provide optimized classification. The best results were obtained by the QSVC using ZZ feature maps and linear entanglement, which had a precision of 1.0 and a notable decrease in false positives. In order to improve fraud detection systems' accuracy and dependability and offer strong solutions to financial institutions, this study shows how quantum computing has the potential to completely transform cybersecurity.

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Chatterjee, Rupak, and Ting Yu. "Generalized coherent states, reproducing kernels, and quantum support vector machines." Quantum Information and Computation 17, no. 15&16 (2017): 1292–306. http://dx.doi.org/10.26421/qic17.15-16-3.

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The support vector machine (SVM) is a popular machine learning classification method which produces a nonlinear decision boundary in a feature space by constructing linear boundaries in a transformed Hilbert space. It is well known that these algorithms when executed on a classical computer do not scale well with the size of the feature space both in terms of data points and dimensionality. One of the most significant limitations of classical algorithms using non-linear kernels is that the kernel function has to be evaluated for all pairs of input feature vectors which themselves may be of substantially high dimension. This can lead to computationally excessive times during training and during the prediction process for a new data point. Here, we propose using both canonical and generalized coherent states to calculate specific nonlinear kernel functions. The key link will be the reproducing kernel Hilbert space (RKHS) property for SVMs that naturally arise from canonical and generalized coherent states. Specifically, we discuss the evaluation of radial kernels through a positive operator valued measure (POVM) on a quantum optical system based on canonical coherent states. A similar procedure may also lead to calculations of kernels not usually used in classical algorithms such as those arising from generalized coherent states.

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Senekane, Makhamisa, and Benedict Molibeli Taele. "Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm." Smart Grid and Renewable Energy 07, no. 12 (2016): 293–301. http://dx.doi.org/10.4236/sgre.2016.712022.

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Anguita, Davide, Sandro Ridella, Fabio Rivieccio, and Rodolfo Zunino. "Quantum optimization for training support vector machines." Neural Networks 16, no. 5-6 (2003): 763–70. http://dx.doi.org/10.1016/s0893-6080(03)00087-x.

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Sarah, L. Johnson. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 31–41. https://doi.org/10.5281/zenodo.15147384.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis. 

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Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis.

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Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.

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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.

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Zhu, Junqi, Li Yang, Xue Wang, et al. "Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM." International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12869. http://dx.doi.org/10.3390/ijerph191912869.

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Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

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Wang, Hong Kai, Ji Sheng Ma, Li Qing Fang, Da Lin Wu, and Yan Feng Yang. "Application of the Least Squares Support Vector Machine for Life Prediction of Vital Parts." Applied Mechanics and Materials 584-586 (July 2014): 2129–32. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.2129.

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In order to better study the wear state of vital parts of the large scale equipment, and overcoming the disadvantage of small sample of vital parts, we use the least squares support vector machine (LS_SVM) algorithm to predict the wear state of vital parts. Using of quantum particle swarm optimization (QPSO) to optimize parameters least squares support vector machine, and achieved good results. Compared those with the method that use of curve fitting to predict the data development trend, show that this method is superior to the curve fitting method, and has good application value.

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Li, Tongyang, Chunhao Wang, Shouvanik Chakrabarti, and Xiaodi Wu. "Sublinear Classical and Quantum Algorithms for General Matrix Games." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8465–73. http://dx.doi.org/10.1609/aaai.v35i10.17028.

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We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix, sublinear algorithms for the matrix game were previously known only for two special cases: (1) the maximizing vectors live in the L1-norm unit ball, and (2) the minimizing vectors live in either the L1- or the L2-norm unit ball. We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed q between 1 and 2, we solve, within some additive error, matrix games where the minimizing vectors are in an Lq-norm unit ball. We also provide a corresponding sublinear quantum algorithm that solves the same task with a quadratic improvement in dimensions of the maximizing and minimizing vectors. Both our classical and quantum algorithms are optimal in the dimension parameters up to poly-logarithmic factors. Finally, we propose sublinear classical and quantum algorithms for the approximate Carathéodory problem and the Lq-margin support vector machines as applications.

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Kerenidis, Iordanis, Anupam Prakash, and Dániel Szilágyi. "Quantum algorithms for Second-Order Cone Programming and Support Vector Machines." Quantum 5 (April 8, 2021): 427. http://dx.doi.org/10.22331/q-2021-04-08-427.

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We present a quantum interior-point method (IPM) for second-order cone programming (SOCP) that runs in time O~(nrζκδ2log⁡(1/ϵ)) where r is the rank and n the dimension of the SOCP, δ bounds the distance of intermediate solutions from the cone boundary, ζ is a parameter upper bounded by n, and κ is an upper bound on the condition number of matrices arising in the classical IPM for SOCP. The algorithm takes as its input a suitable quantum description of an arbitrary SOCP and outputs a classical description of a δ-approximate ϵ-optimal solution of the given problem.Furthermore, we perform numerical simulations to determine the values of the aforementioned parameters when solving the SOCP up to a fixed precision ϵ. We present experimental evidence that in this case our quantum algorithm exhibits a polynomial speedup over the best classical algorithms for solving general SOCPs that run in time O(nω+0.5) (here, ω is the matrix multiplication exponent, with a value of roughly 2.37 in theory, and up to 3 in practice). For the case of random SVM (support vector machine) instances of size O(n), the quantum algorithm scales as O(nk), where the exponent k is estimated to be 2.59 using a least-squares power law. On the same family random instances, the estimated scaling exponent for an external SOCP solver is 3.31 while that for a state-of-the-art SVM solver is 3.11.

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Tharwat, Alaa, and Aboul Ella Hassanien. "Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine." Journal of Classification 36, no. 3 (2019): 576–98. http://dx.doi.org/10.1007/s00357-018-9299-1.

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Bansal, Riya, Nikhil Kumar Rajput, and Megha Khanna. "Enhancing quantum support vector machine for healthcare applications using custom feature maps." Knowledge-Based Systems 320 (June 2025): 113669. https://doi.org/10.1016/j.knosys.2025.113669.

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Guntuka, Sunny. "Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 1455–60. http://dx.doi.org/10.22214/ijraset.2024.64377.

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Abstract: This comprehensive article explores the burgeoning field of Quantum Machine Learning (QML), examining its foundational principles, key algorithms, and potential applications. We delve into the fundamentals of quantum computing, including qubits, quantum gates, and the challenges of quantum measurement and decoherence. The article provides an indepth analysis of Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), comparing them with their classical counterparts and highlighting their unique advantages and limitations. We investigate the promising applications of QML in material science, cryptography, and optimization problems, showcasing its potential to revolutionize drug discovery, secure communication, and complex problem-solving. The review also addresses the significant challenges facing QML, including hardware constraints, error correction, algorithm development, and integration with classical systems. By synthesizing current research and identifying future directions, this article offers a comprehensive overview of QML's transformative potential in artificial intelligence and computational science, while acknowledging the hurdles that must be overcome to fully realize its capabilities.

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Tychola, Kyriaki A., Theofanis Kalampokas, and George A. Papakostas. "Quantum Machine Learning—An Overview." Electronics 12, no. 11 (2023): 2379. http://dx.doi.org/10.3390/electronics12112379.

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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.

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Ding, Chen, Tian-Yi Bao, and He-Liang Huang. "Quantum-Inspired Support Vector Machine." IEEE Transactions on Neural Networks and Learning Systems, 2021, 1–13. http://dx.doi.org/10.1109/tnnls.2021.3084467.

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Park, Siheon, Daniel K. Park, and June-Koo Kevin Rhee. "Variational quantum approximate support vector machine with inference transfer." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-29495-y.

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AbstractA kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.

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