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Conventional models—including classical Support Vector Machines, Random Forests, and deep learning—struggle to capture nonlinear, high-dimensional structures in high-frequency datasets, resulting in weak predictive performance. The paper presents a supervised Quantum Support Vector Machine using quantum feature mapping into a high-dimensional Hilbert space and a quantum-classical hybrid optimization loop. Benchmarks on accuracy, RMSE, MAPE, latency, and Sharpe ratio show improved results and reduced error.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/supervised-quantum-support-vector-algorithms-for-high-frequency-financial-forecasting/648909/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/supervised-quantum-support-vector-algorithms-for-high-frequency-financial-forecasting/648909.png","ImageObject",300,407,{"name":92,"@type":93},"pop out","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"Why are conventional machine learning models insufficient for high-frequency financial forecasting?","Question",{"text":111,"@type":112},"They often fail to capture nonlinear and high-dimensional patterns present in high-frequency datasets, which leads to poor prediction performance.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What approach does the paper propose for forecasting?",{"text":116,"@type":112},"It proposes a supervised Quantum Support Vector Machine (QSVM) that uses quantum feature mapping into high-dimensional Hilbert space and trains via a quantum-classical hybrid optimization loop.",{"name":118,"@type":109,"acceptedAnswer":119},"How is the proposed QSVM evaluated against other methods?",{"text":120,"@type":112},"The paper compares QSVM with classical SVM, Random Forest, LSTM, and hybrid QML using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},648909,1792796084,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":128,"read_time":142},962090760408,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","[www.svedbergopen.com](www.svedbergopen.com) International Journal of Artificial Intelligence and Machine Learning  \nSvedbergOpen  \nDISSEMINATION OF KNOWLEDGE  \nInternational Journal of Artificial Intelligence and Machine Learning  \nPublisher's Home Page: [https://www.svedbergopen.com/](https://www.svedbergopen.com/)  \nResearch Paper Open Access  \nSupervised Quantum Support Vector Algorithms for High Frequency Financial Forecasting  \nDr.E. Mohanraj1*, K. Anitha2, Dr.S. Karunakaran3, S. Suganya4, Dr. Shinki Katyayani Pandey5  \n1* Professor, School of Computing, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.  \nE-mail: [csemohanraj@gmail.com](csemohanraj@gmail.com)  \n2Associate Professor, Department of Management Studies, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Tamil Nadu, India. E-mail: [anithak@maher.ac.in](anithak@maher.ac.in)  \n3Associate Professor, Department of Management Studies, St.Joseph's Institute of Technology, OMR, Chennai, Tamil Nadu, India. E[mail: karunakaranmba.s@gmail.com](mail: karunakaranmba.s@gmail.com), [https://orcid.org/0009-0009-2478-5352](https://orcid.org/0009-0009-2478-5352)  \n4Assistant Professor, Department of Management Studies, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Tamil Nadu, [India. E-mail: ssuganyamba@maher.ac.in](India. E-mail: ssuganyamba@maher.ac.in)  \n5Assistant Professor, Kalinga University, Naya Raipur, Chhattisgarh, India. E-mail: [ku.shinkikatyayanipandey@kalingauniversity.ac.in](ku.shinkikatyayanipandey@kalingauniversity.ac.in), Joseph's Institute of Technology, OMR, Chennai, Tamil Nadu, India. [E-mail: karunakaranmba.s@gmail.com](E-mail: karunakaranmba.s@gmail.com), [https://orcid.org/0009-0009-9316-5093](https://orcid.org/0009-0009-9316-5093)  \nAbstract  \nHigh-Frequency Financial Forecasting is important in algorithmic trading as it helps make decisions based on high-frequency data in milliseconds. This challenge has been found to be unsolvable using conventional machine learning approaches, such as classical Support Vector Machines (SVMs), Random Forest (RF), and deep learning models, which were not able to capture the nonlinear and highdimensional patterns embedded in the high-frequency datasets, leading to poor prediction performance. This paper proposes a Supervised Quantum Support Vector Machine (QSVM) model, which is tailored for the high-frequency financial forecasting task. The QSVM can effectively encode the market information and enhance the predictability while maintaining the computational efficiency through quantum feature mapping and high-dimensional Hilbert space. This process includes encoding of tick-level market microstructure data, stock and futures prices, and technical indicators into quantum states and subsequent training using a quantumclassical hybrid optimization loop. Classical SVM, Random Forest, LSTM, and hybrid QML are used as the benchmarks for the proposed QSVM using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio. The results obtained from the experiment show that the QSVM has better accuracy (91.2%), RMSE (0.018), and latency (22 ms) as well. Hence, the QSVM shows better performance when compared to other methods and is capable of modeling the markets that have a fast change in dynamics. High-dimensional quantum kernel plays a part in enhancing the ability to separate non-linearities. Therefore, the QSVM could be a potential solution to apply in algorithmic trading. The limitations are hardware constraints of NISQ and restrictions on the size of the dataset. Future directions of research will include the scalability of the model and the use of ensemble approaches.  \nkeywords: High-frequency trading, Quantum Support Vector Machine, Algorithmic trading, Quantum feature mapping, Predictive accuracy, Hybrid quantum-classical optimization, financial forecasting.  \n1. Introduction  \nHigh-frequency fin","cbCaif2vpk6cWTsR","https://ap.wps.com/l/cbCaif2vpk6cWTsR","pdf",566324,"English","# Introduction\n## Related Work","[{\"question\":\"Why are conventional machine learning models insufficient for high-frequency financial forecasting?\",\"answer\":\"They often fail to capture nonlinear and high-dimensional patterns present in high-frequency datasets, which leads to poor prediction performance.\"},{\"question\":\"What approach does the paper propose for forecasting?\",\"answer\":\"It proposes a supervised Quantum Support Vector Machine (QSVM) that uses quantum feature mapping into high-dimensional Hilbert space and trains via a quantum-classical hybrid optimization loop.\"},{\"question\":\"How is the proposed QSVM evaluated against other methods?\",\"answer\":\"The paper compares QSVM with classical SVM, Random Forest, LSTM, and hybrid QML using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio.\"}]","Supervised Quantum Support Vector Algorithms for High Frequency Financial Forecasting | PDF",13]