[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122501-en":3,"doc-seo-122501-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122501,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Quantum-Enhanced Feature Maps for Improved Quantum Kernels and Advanced Quantum Machine Learning Application - Published poster session summary","Quantum-Enhanced Feature Maps for Improved Quantum Kernels and Advanced Quantum Machine Learning Application presents a quantum-enhanced kernel approach aimed at leveraging quantum computation for complex machine learning tasks. The work motivates improved performance on high-dimensional, spatio-temporal brain data and introduces a novel nonlinear feature map built from unitary operations using Pauli operators and Hadamard-gate based constructions. Analytical evaluations compare multiple feature maps on Circle, Moon, and XOR datasets using 5-fold cross-validation mean accuracy, then extend the approach to a hybrid classical-quantum neuromorphic application and conclude with evidence of robustness through feature-map design and hyper-parameter tuning.","Quantum-Enhanced Feature Maps for Improved Quantum Kernels and Advanced Quantum Machine Learning Application  \nJha, R. K. , Kasabov, N. , Bhattacharyya, S. , Coyle, D. , & Prasad, G. (2024) . Quantum-Enhanced Feature Maps for Improved Quantum Kernels and Advanced Quantum Machine Learning Application. Poster session presented at Quantum Computing and Artificial Intelligence Applications Workshop, Copenhagen, Denmark. Advance online publication.  \nLink to publication record in Ulster University Research Portal  \nPublication Status:  \nPublished online: 06/05/2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nFor Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW)  \nWhen citing an AAM published under Ulster University's RRPSW please use the following citation structure:  \nAuthor, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0.  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. 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If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 03/08/2026  \nQuantum-Enhanced Feature Maps for Improved Quantum Kernels and  \nAdvanced Quantum Machine Learning Application  \nRavi Kumar Jha1 , Nikola Kasabov 1 , 2 , Saugat Bhattacharyya 1 , Damien Coyle 1 , 3 , Girijesh Prasad 1  \n1 Intelligent Systems Research Centre, Ulster University, Londonderry, United Kingdom  \n2 Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand  \n3 Institute for the Augmented Human, University of Bath, United Kingdom  \nMotivation  \nTo leverage the advantages of quantum computation in complex machine learning tasks for advanced applications such as neurosciences and neuromorphic technologies.  \nQuantum Mechanical Properties  \n| Superposition |  |\n| --- | --- |\n|  |  |\n| Parallelism |  |\n\n| Entanglement |  |\n| --- | --- |\n|  |  |\n| Speedup |  |\n\nPotential Quantum Advantage  \nObjective  \n➢ To develop quantum-enhanced kernels for high-dimensional, complex, spatio-temporal brain data to improve classification and prediction tasks.  \n➢ To demonstrate the proof of concept of methodology using various two-dimensional benchmark datasets, and an advanced application.  \nQuantum Kernels  \n→ A quantum kernel utilizes a feature map to transform the input, 􀝔 ∈ℝ􀯡 into the quantum state space as follows:  \n􀜷 Φ () = exp 􀝅 ෍􀯌⊆ 􀯡 α 􀯜 􀟶 􀯌  ς 􀯜∈􀯌􀜲􀯜 ,  \nwhere 􀜲􀯜 ∈ 􀜫 􀜺 􀜻 􀜼 are the Pauli operators, and α is the rotational factor for 􀜵 􀝋􀝎􀝀􀝁􀝎 expansions, where |􀜵| ≤ 2 .  \nA general 􀜰 − 􀝍􀝑􀜾􀝅􀝐 feature map is defined by the unitary operator →  \n􀜷 􀰃 􀝔 with the Hadamard gate as:  \n􀜷Φ  = 􀜷 Φ  􀜪⊗􀯇 􀜷Φ  􀜪⊗􀯇 .  \nHere, Φ  =  􀟶 1 􀝔 , 􀟶 2 􀝔 , 􀟶 1, 2 􀝔  is the nonlinear 􀝁􀝊􀜿􀝋􀝀􀝅􀝊􀝃1 .  \nFeature Maps  \nA suitable feature map is instrumental in achieving a quantumenhanced kernel. Here, we propose a novel nonlinear feature map as:  \n􀟶 􀯜 􀝔 = 􀝔 􀯜 􀜽􀝊􀝀 􀟶 1 2 􀝔 =","cbCaiik4LiAbG8ZM","https://ap.wps.com/l/cbCaiik4LiAbG8ZM","pdf",1053324,1,2,"English","en",105,"# Motivation\n# Quantum Mechanical Properties\n# Potential Quantum Advantage - Objective\n# Quantum Kernels\n## Feature Maps\n# Results - Performance Analysis\n# Advanced Neuromorphic Application\n# Conclusion\n# Acknowledgment\n# References","[{\"question\":\"What is the main goal of the proposed quantum-enhanced approach?\",\"answer\":\"To develop quantum-enhanced kernels using feature maps that improve classification and prediction for high-dimensional, complex spatio-temporal brain data, while demonstrating proof of concept on benchmark datasets and an advanced application.\"},{\"question\":\"How does a quantum kernel use a feature map in this work?\",\"answer\":\"A quantum kernel transforms input data into a quantum state space via a feature map expressed with unitary operators, including constructions based on Pauli operators and Hadamard-gate components.\"},{\"question\":\"How is performance evaluated for different quantum feature maps?\",\"answer\":\"Performance is analyzed analytically across multiple quantum kernels using three 2-D nonlinear benchmark datasets (Circle, Moon, XOR) and classification accuracy assessed with 5-fold cross-validation mean accuracy.\"}]","Quantum-Enhanced Feature Maps for Improved Quantum Kernels and Advanced Quantum Machine Learning Application - Published poster session summary | PDF",1785810977,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"quantum-enhanced-feature-maps-for-improved-quantum-kernels-and-advanced-quantum-machine-learning-application-published-poster-session-summary","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/quantum-enhanced-feature-maps-for-improved-quantum-kernels-and-advanced-quantum-machine-learning-application-published-poster-session-summary/122501/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the proposed quantum-enhanced approach?","Question",{"text":74,"@type":75},"To develop quantum-enhanced kernels using feature maps that improve classification and prediction for high-dimensional, complex spatio-temporal brain data, while demonstrating proof of concept on benchmark datasets and an advanced application.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does a quantum kernel use a feature map in this work?",{"text":79,"@type":75},"A quantum kernel transforms input data into a quantum state space via a feature map expressed with unitary operators, including constructions based on Pauli operators and Hadamard-gate components.",{"name":81,"@type":72,"acceptedAnswer":82},"How is performance evaluated for different quantum feature maps?",{"text":83,"@type":75},"Performance is analyzed analytically across multiple quantum kernels using three 2-D nonlinear benchmark datasets (Circle, Moon, XOR) and classification accuracy assessed with 5-fold cross-validation mean accuracy.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]