[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31235":3,"doc-seo-31235":27},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31235,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification","Image classification is crucial in remote sensing, yet large-scale Earth observation data analysis is constrained by the high computational demands of sophisticated machine learning models. The study proposes a hybrid quantum-classical convolutional neural network (QC-CNN) that uses quantum computing to extract high-level critical features from Earth observation inputs. An amplitude encoding strategy reduces required quantum-bit resources. Complexity analysis suggests convolution acceleration versus classical methods, and experiments on multiple EO benchmarks validate improved accuracy and generalizability via TensorFlow Quantum.","cbCain6qTDymq5P2","https://ap.wps.com/l/cbCain6qTDymq5P2","pdf",1934501,1,15,"English","en","# Introduction\n## Image classification in Earth observation\n## Quantum machine learning background\n# Proposed QC-CNN approach\n## Quantum feature extraction\n## Amplitude encoding resource reduction\n# Complexity and experimental evaluation\n## Benchmark datasets and platform","[{\"question\":\"What problem does the QC-CNN address in remote sensing image classification?\",\"answer\":\"It targets the computational bottleneck of using sophisticated classical machine learning models on large volumes of Earth observation data. The approach leverages quantum properties to better handle the workload for classification tasks.\"},{\"question\":\"How does amplitude encoding help in the proposed model?\",\"answer\":\"Amplitude encoding reduces the required quantum bit resources. This makes the quantum feature extraction more resource-efficient for the hybrid QC-CNN.\"},{\"question\":\"How is the model evaluated and what datasets are used?\",\"answer\":\"Performance is assessed on multiple Earth observation benchmarks, including OverheadMNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2. Experiments are conducted through the TensorFlow Quantum platform, comparing against the classical counterpart.\"}]",1779224476,38,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"hybrid-quantum-classical-convolutional-neural-network-model-for-image-classification","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/hybrid-quantum-classical-convolutional-neural-network-model-for-image-classification/31235/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-19",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does the QC-CNN address in remote sensing image classification?","Question",{"text":73,"@type":74},"It targets the computational bottleneck of using sophisticated classical machine learning models on large volumes of Earth observation data. The approach leverages quantum properties to better handle the workload for classification tasks.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does amplitude encoding help in the proposed model?",{"text":78,"@type":74},"Amplitude encoding reduces the required quantum bit resources. This makes the quantum feature extraction more resource-efficient for the hybrid QC-CNN.",{"name":80,"@type":71,"acceptedAnswer":81},"How is the model evaluated and what datasets are used?",{"text":82,"@type":74},"Performance is assessed on multiple Earth observation benchmarks, including OverheadMNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2. Experiments are conducted through the TensorFlow Quantum platform, comparing against the classical counterpart.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]