[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118752-en":3,"doc-seo-118752-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118752,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Quantum machine learning for image classification - Abstract and study overview","Image recognition and classification are core tasks with broad real-world impact across industries. This work advances hybrid quantum-classical approaches by proposing two neural-network models for image classification: one using parallel quantum layers and another using a quanvolutional layer. Both designs focus on trainable variational parameters and efficient learning, including partitioning the quantum component into multiple parallel variational circuits. One model reaches over 99% accuracy on MNIST, with results highlighting the potential of hybrid quantum-classical models to improve classification in domains such as healthcare, security, and marketing.","arXiv :2304 .09224v1 [ quant-ph] 18 Apr 2023  \nQuantum machine learning for image classi􀀌cation  \nArsenii Senokosov, Alexander Sedykh, Asel Sagingalieva, and Alexey Melnikov  \nTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, Switzerland  \nImage recognition and classi􀀌cation are fundamental tasks with diverse practical applications across various industries, making them critical in the modern world. Recently, machine learning models, particularly neural networks, have emerged as powerful tools for solving these problems. However, the utilization of quantum e􀀋ects through hybrid quantum-classical approaches can further enhance the capabilities of traditional classical models. Here, we propose two hybrid quantum-classical models: a neural network with parallel quantum layers and a neural network with a quanvolutional layer, which address image classi􀀌cation problems. One of our hybrid quantum approaches demonstrates remarkable accuracy of more than 99% on the MNIST dataset. Notably, in the proposed quantum circuits all variational parameters are trainable, and we divide the quantum part into multiple parallel variational quantum circuits for e􀀎cient neural network learning. In summary, our study contributes to the ongoing research on improving image recognition and classi􀀌cation using quantum machine learning techniques. Our results provide promising evidence for the potential of hybrid quantum-classical models to further advance these tasks in various 􀀌elds, including healthcare, security, and marketing.  \nIntroduction  \nImage classi􀀌cation is a critical task in the modern world due to its wide range of practical applications in various 􀀌elds [1] . For instance, in medical imaging, image classi􀀌cation algorithms have been shown to significantly improve the accuracy and speed of diagnoses of many diseases [2, 3] . In the 􀀌eld of autonomous vehicles, image classi􀀌cation plays a crucial role in object detection, tracking, and classi􀀌cation, which is necessary for safe and e􀀎cient navigation.  \nDeep learning approaches [4] like deep convolutional neural networks (CNNs) have emerged as powerful tools for image classi􀀌cation and recognition tasks [5, 6], achieving state-of-the-art performance on various benchmark datasets [7, 8] . However, as the amount of visual data increases, modern neural networks are facing significant computational challenges.  \nQuantum technologies, on the other hand, o􀀋er the potential to overcome this computational limitation by harnessing the power of quantum mechanics to perform computations in parallel [9] . Quantum machine learning (QML) is a rapidly evolving 􀀌eld that combines the principles of quantum mechanics and classical machine learning [10, 11] . This 􀀌eld has the potential to revolutionize various areas of computing, including imageclassi􀀌cation [12, 13] . It has attracted signi􀀌cant attention due to its potential to solve computational problems that classical computers are unable to solve e􀀎ciently [9] . This potential arises from the unique features of quantum computing, such as superposition and entanglement, which can provide an exponential speedup for speci􀀌c machine learning tasks [14] . Moreover, QML algorithms produce probabilistic results, which is very natural forclassi􀀌cation problems [15] and also act in an exponentially bigger search space, which greatly increases their performance [16{18] . However, the real-world implementation of quantum algorithms faces signi􀀌cant challenges, such as the need for error correction and the high sensi-  \ntivity of quantum systems to external disturbances [19] . Despite these challenges, QML has shown promising results in several applications [20] . In the context of imageclassi􀀌cation, QML algorithms can process large datasets of images more e􀀎ciently than classical algorithms, leading to faster and more accurate classi􀀌cation [21] .  \nA promising area of research within QML for image classi􀀌cation is the hybrid quantum neural network (HQNN) [","cbCaifYPm2UU3ikJ","https://ap.wps.com/l/cbCaifYPm2UU3ikJ","pdf",1206045,1,9,"English","en",105,"# Introduction\n## Motivation and background\n## Hybrid quantum neural networks (HQNN)\n## Proposed approaches\n# Experimental results and conclusions","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses image recognition and image classification, focusing on how to improve classification performance using hybrid quantum-classical neural network models.\"},{\"question\":\"What two hybrid quantum-classical models are proposed?\",\"answer\":\"The study proposes (1) a neural network with parallel quantum layers and (2) a neural network with a quanvolutional layer, both designed for image classification.\"},{\"question\":\"How well do the models perform on the MNIST dataset?\",\"answer\":\"One hybrid quantum approach achieves more than 99% accuracy on MNIST, demonstrating strong classification capability. The other model reports accuracy comparable to its classical counterpart despite fewer trainable parameters.\"}]","Quantum machine learning for image classification - Abstract and study overview | PDF",1785720056,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-machine-learning-for-image-classification-abstract-and-study-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/quantum-machine-learning-for-image-classification-abstract-and-study-overview/118752/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses image recognition and image classification, focusing on how to improve classification performance using hybrid quantum-classical neural network models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What two hybrid quantum-classical models are proposed?",{"text":80,"@type":76},"The study proposes (1) a neural network with parallel quantum layers and (2) a neural network with a quanvolutional layer, both designed for image classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How well do the models perform on the MNIST dataset?",{"text":84,"@type":76},"One hybrid quantum approach achieves more than 99% accuracy on MNIST, demonstrating strong classification capability. 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