Relevant bibliographies by topics / Quantum support vector machine

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Quantum support vector machine'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Quantum support vector machine"

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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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Dissertations / Theses on the topic "Quantum support vector machine"

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Pang, Bo. "Handwriting Chinese character recognition based on quantum particle swarm optimization support vector machine." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950620.

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Yang, Jiaying. "Support Vector Machines on Noisy Intermediate-Scale Quantum Computers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266112.

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Support vector machine algorithms are considered essential for the implementationof automation in a radio access network. Specifically, they are critical inthe prediction of the quality of user experience for video streaming based ondevice and network-level metrics. Quantum support vector machine (QSVM)is the quantum analogue of the classical support vector machine algorithm,which utilizes the properties of quantum computers to exponentially speed upthe algorithm. This thesis provides an implementation of the QSVMclassificationsystem and its fundamental components, the quantum Fourier transform(QFT) and the Harrow-Hassidim-Lloyd (HHL) algorithms, using the opensourcequantum computing software development kits (SDKs), IBM’s Qiskitand Rigetti’s pyQuil, and real quantum computers that can be accessed by publiccloud service. Moreover, the QSVM classification system is implementedon a superconducting quantum computer, IBMQX2, showing the potential ofthis quantum algorithm to be executed on the Noisy Intermediate-Scale Quantum(NISQ) computers.<br>Supportvektormaskinalgoritmer anses nödvändiga för implementering av automatiseringi radionätet. De är kritiska när det gäller att säkerställa den upplevdaanvändarkvaliteten för strömmad video (quality of user experience) baseradpå enhets- och nätverksnivåmätningar. Kvantsupportvektormaskinsalgoritmen(QSVM) är en kvantanaloga version av den klassiska supportvektormaskinalgoritmen,som använder egenskaperna hos kvantdatorer för att exponentielltsnabba upp algoritmen. Denna avhandling tillhandahåller en implementeringav den QSVM klassificeringssystemet och dess grundläggande komponenter,kvant-Fourier-transform (QFT) och Harrow-Hassidim-Lloyd (HHL) -algoritmerna, med hjälp av open-source kvantmjukvara (SDK), IBMs Qiskitoch Rigettis pyQuil och riktiga kvantdatorer som kan nås via en offentlig molntjänst.Dessutom implementeras QSVM-klassificeringssystemet på en supraledandekvantdator, IBMQX2, som visar potentialen för denna kvantalgoritmatt kunna exekveras på den brusiga medelstora kvantdatorer (NISQ).
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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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McChesney, Charlie. "External Support Vector Machine Clustering." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/409.

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The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters.
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Armond, Kenneth C. Jr. "Distributed Support Vector Machine Learning." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.

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Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM implementations must contend with some kind of chunking process to train parts of the data at a time (10 chunks of 1000, for example, to learn the 10,000). Sequential and multi-threaded chunking methods provide a way to run the SVM on large datasets while retaining accuracy. The multi-threaded distributed SVM described in this thesis is implemented using Java RMI, and has been developed to run on a network of multi-core/multi-processor computers.
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Zigic, Ljiljana. "Direct L2 Support Vector Machine." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.

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This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative. Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art algorithms. The training time improvement is achieved at no cost, in other words, the accuracy is kept at the same level. All the experiments that support this claim were conducted on various datasets within the strict double cross-validation scheme. DL2 SVM solved with NN ISDA has faster training time on both medium and large datasets. In addition to a comprehensive DL2 SVM model we introduce and derive its three variants. Three different solvers for the DL2's system of linear equations with nonnegativity constraints were implemented, presented and compared in this dissertation.
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Wen, Tong 1970. "Support Vector Machine algorithms : analysis and applications." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.<br>Includes bibliographical references (p. 89-97).<br>Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained Quadratic Programming (QP) problem. However, in practice its dual problem is actually computed. An important property of the dual QP problem is that its solution is sparse. The training examples that determine the SVM classifier are known as support vectors (SVs). Motivated by the geometric derivation of the primal QP problem, we investigate how the dual problem is related to the geometry of SVs. This investigation leads to a geometric interpretation of the scaling property of SVMs and an algorithm to further compress the SVs. A random model for the training examples connects the Hessian matrix of the dual QP problem to Wishart matrices. After deriving the distributions of the elements of the inverse Wishart matrix Wn-1(n, nI), we give a conjecture about the summation of the elements of Wn-1(n, nI). It becomes challenging to solve the dual QP problem when the training set is large. We develop a fast algorithm for solving this problem. Numerical experiments show that the MATLAB implementation of this projected Conjugate Gradient algorithm is competitive with benchmark C/C++ codes such as SVMlight and SvmFu. Furthermore, we apply SVMs to time series data.<br>(cont.) In this application, SVMs are used to predict the movement of the stock market. Our results show that using SVMs has the potential to outperform the solution based on the most widely used geometric Brownian motion model of stock prices.<br>by Tong Wen.<br>Ph.D.
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Liu, Yufeng. "Multicategory psi-learning and support vector machine." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1085424065.

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Thesis (Ph. D.)--Ohio State University, 2004.<br>Title from first page of PDF file. Document formatted into pages; contains x, 71 p.; also includes graphics Includes bibliographical references (p. 69-71). Available online via OhioLINK's ETD Center
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Tsang, Wai-Hung. "Scaling up support vector machines /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.

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Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.

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Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.
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Books on the topic "Quantum support vector machine"

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Campbell, Colin. Learning with support vector machines. Morgan & Claypool, 2011.

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Hamel, Lutz. Knowledge discovery with support vector machines. John Wiley & Sons, 2009.

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Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Nova Science Publishers, 2011.

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K, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Taylor & Francis, 2014.

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Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. MIT Press, 1999.

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Joachims, Thorsten. Learning to classify text using support vector machines. Kluwer Academic Publishers, 2002.

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Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. VDM Verlag Dr. Müller, 2009.

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J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press, 2002.

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Terzic, Jenny. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach. Springer International Publishing, 2013.

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Martinez-Ramon, Manuel. Support vector machines for antenna array processing and electromagnetics. Morgan & Claypool Publishers, 2006.

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Book chapters on the topic "Quantum support vector machine"

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Bauckhage, Christian, and Rafet Sifa. "Training Support Vector Machines by Solving Differential Equations." In Cognitive Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6_12.

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Abstract The increasingly popular idea of Physics Informed Machine Learning uses trained machine learning models as tools for differential equation solving. Here, we turn this idea upside down and consider differential equation solving as a tool for training machine learning models. We focus on support vector machines for binary classification and explore the merits of training them by means of solving gradient flows. We thus assume a continuous time perspective on a fundamental machine learning problem which, in the mid- to long term, may inform implementations on (re)emerging hardware platforms such as analog- or quantum computers.
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Piatkowski, Nico, and Sascha Mücke. "Real-Part Quantum Support Vector Machines." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70371-3_9.

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Acampora, Giovanni, and Autilia Vitiello. "Hand Gesture Recognition from sEMG Signals Through Quantum Support Vector Machine." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4589-3_16.

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Feng, Xiao, Jincheng Li, Changgui Huang, et al. "Quantum Algorithm for Support Vector Machine with Exponentially Improved Dependence on Precision." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24268-8_53.

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Chen, Kuan-Cheng, Xiaotian Xu, Henry Makhanov, Hui-Hsuan Chung, and Chen-Yu Liu. "Quantum-Enhanced Support Vector Machine for Large-Scale Multi-class Stellar Classification." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5609-4_12.

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Bhatia, Harshil Singh, and Frank Phillipson. "Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer." In Computational Science – ICCS 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77980-1_7.

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Marghany, Maged. "Quantum Support Vector Machine for Automatic Detection of COVID-19 Lung Infection Features." In Advanced Remote Sensing Technology for Covid-19 Monitoring and Forecasting. CRC Press, 2025. https://doi.org/10.1201/9781003351009-5.

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Marghany, Maged. "Quantum Support Vector Machine in Retrieving Clay Mineral Saturation in Multispectral Sentinel-2 Satellite Data." In Remote Sensing and Image Processing in Mineralogy. CRC Press, 2021. http://dx.doi.org/10.1201/9781003033776-7.

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Agarwal, Preeti, and Mansaf Alam. "Quantum-Inspired Support Vector Machines for Human Activity Recognition in Industry 4.0." In Proceedings of Data Analytics and Management. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6289-8_24.

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Rodríguez-Díaz, Francesc, José Francisco Torres, David Gutiérrez-Avilés, Alicia Troncoso, and Francisco Martínez-Álvarez. "An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines." In Advances in Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62799-6_13.

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Conference papers on the topic "Quantum support vector machine"

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Jeong, Suhui, Sanghyun Kim, and Jiwon Seo. "Quantum Support Vector Machine-Based Classification of GPS Signal Reception Conditions." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10390.

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Bharattej R, Rana Veer Samara Sihman, Ramy Riad Al-Fatlawy, S. Meenakshi Sundaram, E. Annie Rathnakumari, and M. N. Sudha. "Quantum Variational Based Support Vector Machine for Early Detection of Sepsis." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872228.

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Rambharat, Yadav Abhishek, Dev Ritesh Shelke, Kundlik Adnan Khaleel, Anande Aditya Arunkumar, and Deepa Ekhande. "SmartGuard: Support Vector Machine(SVM)-Powered Defense Mechanism for Phishing Prevention." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882417.

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Camargo, Jairo Alberto Fuentes, Jose Aguilar, and Ángel Pinto. "Analysis of Quantum Support Vector Machines in Classification Problems." In 2024 L Latin American Computer Conference (CLEI). IEEE, 2024. http://dx.doi.org/10.1109/clei64178.2024.10700573.

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Dutta, Shivanya Shomir, Sahil Sandeep, S. Sridevi, Mohan Ram Sridhar, and Chaitanya Singh. "Quantum Kernel based Support Vector Machine with Quantum Approximate Optimization Algorithm Embedding for improved Lung Cancer Prediction." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725077.

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S, Sharon Jessika, Harsh Mantri, Angad Chaudhry, et al. "Revolutionizing Heart Disease Prediction with Quantum-Enhanced Support Vector Machines." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864449.

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Chikhaoui, Belkacem. "Enhancing Classification Accuracy with Quantum Non-Negative Matrix Factorization and Quantum Support Vector Machines." In 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025. https://doi.org/10.1109/qcnc64685.2025.00075.

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Onim, Md Saif Hassan, Travis S. Humble, and Himanshu Thapliyal. "Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10929873.

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Zadeh, Arshia Eftekhari, Ali Mikaeili Barzili, Mohammad R. Nemati, Mohammad Khoshnevisan, Hamid Azadegan, and Behzad Moshiri. "Detection of Autism Spectrum Disorder Using Quantum Support Vector Machines Algorithm." In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E). IEEE, 2025. https://doi.org/10.1109/ai2e64943.2025.10983511.

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C, Chetana, and S. S. Arumugam. "Enhancing Accuracy of Cyberstalking Detection with Novel Convolutional Neural Network Comparison with Support Vector Machine." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545068.

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Reports on the topic "Quantum support vector machine"

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Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/881587.

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Alali, Ali. Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1495.

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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45842.

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This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

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Liu, Y. Support vector machine for the prediction of future trend of Athabasca River (Alberta) flow rate. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/299739.

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Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada458739.

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Luo, Yuzhou, Rui Wang, Zhongwei Jiang, and Xiqing Zuo. Assessment of the Effect of Health Monitoring System on the Sleep Quality by Using Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.01.16.

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Luo, Yuzhou, Rui Wang, Zhongwei Jiang, and Xiqing Zuo. Assessment of the Effect of Health Monitoring System on the Sleep Quality by Using Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/grabs2018.1.16.

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Karlsson, Hyunjoo Kim, and Yushu Li. Investigation of Swedish krona exchange rate volatilityby APARCH-Support Vector Regression. Department of Economics and Statistics, Linnaeus University, 2024. http://dx.doi.org/10.15626/ns.wp.2024.10.

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This paper investigates daily exchange rate volatility behaviors with a focus on a small open economy’s currency, the Swedish krona (SEK), against four currencies: the U.S. dollar, Euro, the Pound Sterling (GBP), and the Norwegian krone (NOK) over the whole period from Jan. 2010 to March 2023, whereas the whole period is divided into different sub-sample periods based on the economic events. In the framework of APARCH models, we find that volatility behavior of the Swedish krona (SEK) exchange rates varies across different currency pairs (SEK being included in all cases) and sub-sample periods. Precisely, a negative asymmetric return-volatility relationship was found for the case of the SEK/EUR exchange rate, while an inverted asymmetric relationship was detected in the case of SEK/NOK exchange rate. Significant asymmetric effects of volatility in the SEK/USD and SEK/GBP exchange rates were not observed for either the whole period or the three sub-sample periods. As the return of exchange rate are all non-normally distributed, we then use a distribution-free support vector machine-based regression, called support vector regression (SVR), to estimate and forecast volatility in the framework of the chosen APARCH model for each krona exchange rate. The result shows that the SVR-APARCH based volatility forecasting performs better than the forecasting based on APARCH model estimated by maximum likelihood estimation (MLE).
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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.
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