[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290890-105":59,"doc-detail-290890-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","quantum-machine-learning-a-review-of-hybrid-classicalquantum-approaches","Quantum Machine Learning - A Review of Hybrid Classical–Quantum Approaches","","Hybrid classical–quantum machine learning (QML) leverages noisy intermediate-scale quantum (NISQ) devices by assigning classical processors to optimisation and large-scale computation while using quantum hardware for feature mapping, nonlinear transformations, and kernel evaluation. The review highlights recent progress in variational quantum circuits, hybrid neural networks, and quantum kernel methods across domains such as molecular modelling, materials discovery, and small-sample learning. Remaining limits include noise, barren plateaus, and costly repeated measurements, motivating noise-aware design, benchmark standards, stronger error mitigation, and longer-term fault tolerance.",{"@graph":69,"@context":123},[70,84,106],{"@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/quantum-machine-learning-a-review-of-hybrid-classicalquantum-approaches/290890/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/quantum-machine-learning-a-review-of-hybrid-classicalquantum-approaches/290890.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What role does hybrid classical–quantum machine learning assign to classical vs quantum components?","Question",{"text":113,"@type":114},"Classical processors handle optimisation and large-scale computation, while quantum hardware performs tasks such as feature mapping, nonlinear transformations, or kernel evaluations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which application areas are mentioned as benefiting from hybrid QML advances?",{"text":118,"@type":114},"The text highlights molecular modelling, materials discovery, and small-sample learning, including uses in computational chemistry, financial modelling, and healthcare analytics.",{"name":120,"@type":111,"acceptedAnswer":121},"What challenges continue to limit scalability and broader adoption?",{"text":122,"@type":114},"Enduring issues include noise, barren plateaus in optimisation landscapes, and the expense of repeated measurements.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290890,1789642901,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Quantum Machine Learning: A Review of Hybrid Classical–Quantum Approaches  \nBhavesh B. Prajapati  & Riddhi B. Prajapati   \nIT Department, L. D. College of Engineering, Commissionerate of Technical Education, Government of Gujarat, India.  \nE-mails: [b.b.prajapati@gmail.com](b.b.prajapati@gmail.com), [to.riddhi@gmail.com](to.riddhi@gmail.com)  \n[Editors: Srikanth Radhakrishna & Danko D](Editors: Srikanth Radhakrishna & Danko D). Georgiev  \nArticle history: Submitted on October 6, 2025; Accepted on January 28, 2026; Published on January 30, 2026 .  \nAn interesting approach to using noisy  \nintermediate-scale quantum (NISQ) de  \nvices is hybrid classical–quantum machine learning (QML). In these methods, classical processors handle optimisation and large-scale computation, while quantum hardware is devoted to tasks like feature mapping, nonlinear transformations, or kernel evaluation. Practical near-term demonstrations are made possible by this division of labour, which also mitigates existing hardware restrictions. In specialised fields like molecular modelling, materials discovery, and small-sample learning issues, recent advancements in variational quantum circuits, hybrid neural networks, and quantum kernel techniques have produced promising outcomes. However, scalability and wider applicability are still hampered by enduring issues including noise, barren plateaus, and the expense of repeated measurements. Long-term developments will require fault tolerance, logical qubits, and established software infrastructures, whereas near-term success depends on noise-aware algorithm design, repeatable experimental benchmarks, and enhanced error-mitigation strategies. When taken asa whole, these advancements show a viable path to achieving quantum advantage in machine learning. Quanta 2026; 15: 1–12.  \n1 Introduction  \nFrom being largely a theoretical idea, quantum computing is now a quickly evolving experimental technology that has the potential to resolve issues that are still computationally unfeasible for classical systems. The evolution of scalable, fault-tolerant quantum algorithms is, however, hampered by several issues with the current generation of noisy intermediate-scale quantum (NISQ) devices, such as short coherence times, imperfect gate fidelities, and limited qubit availability, as shown by John Preskill [1] and further discussed by Marco Cerezo et al. [2] . Hybrid classical–quantum machine learning (QML) has become a useful approach to tackle these issues. Under this paradigm, conventional resources handle optimisation and large-scale data processing, whereas quantum processors are responsible for tasks like feature encoding, nonlinear mappings, or kernel evaluations. With today's hardware, this division of labour enables researchers to investigate significant quantum advantages, as demonstrated by S. Thanasilp et al. [3] and Y. Gujju et al. [4] .  \n This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY-4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nQuanta | DOI: 10.12743/quanta.94 January 2026 | Volume 15 | Page 1  \nThe terrain of hybrid QML is defined by a number of methodological approaches. While quantum kernel techniques take advantage of high-dimensional Hilbert space representations to improve small-data classification problems, variational quantum circuits (VQCs) and quantum–classical neural networks provide parameter optimisation using classical training loops as shown by Marcello Benedetti et al. [5] . Hybrid models are utilized to estimate molecular properties, improve diagnostic outcomes under limited data scenarios, and refine portfolio strategies in application areas such as computational chemistry, financial modelling, and healthcare analytics. In these domains, such techniques have already shown promise, as discussed by R. Prajapati and B. Prajapati [6] . Scala","cbCairpZnMgAIP7q","https://ap.wps.com/l/cbCairpZnMgAIP7q","pdf",534007,12,"English","# Introduction\n# Background\n## Quantum Computing Basics","[{\"question\":\"What role does hybrid classical–quantum machine learning assign to classical vs quantum components?\",\"answer\":\"Classical processors handle optimisation and large-scale computation, while quantum hardware performs tasks such as feature mapping, nonlinear transformations, or kernel evaluations.\"},{\"question\":\"Which application areas are mentioned as benefiting from hybrid QML advances?\",\"answer\":\"The text highlights molecular modelling, materials discovery, and small-sample learning, including uses in computational chemistry, financial modelling, and healthcare analytics.\"},{\"question\":\"What challenges continue to limit scalability and broader adoption?\",\"answer\":\"Enduring issues include noise, barren plateaus in optimisation landscapes, and the expense of repeated measurements.\"}]","Quantum Machine Learning - A Review of Hybrid Classical–Quantum Approaches | PDF"]