[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31228":3,"doc-seo-31228":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},31228,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data","Quantum machine learning is emerging in earth observation and remote sensing, with research largely split between fully quantum and hybrid approaches. Few studies apply quantum methods to earth-observation land cover classification, and none provide practical guidelines for tuning quantum-part hyperparameters. This letter proposes a hybrid quantum neural network with a strategy to select the number of qubits to balance complexity and accuracy. Experiments yield efficient configurations, strong results versus state of the art, improved robustness to dataset imbalance, and freely available code for reproducibility.","cbCaikaKaHAudA1B","https://ap.wps.com/l/cbCaikaKaHAudA1B","pdf",4602898,1,5,"English","en","# Introduction\n## Motivation and background\n## Quantum computing and ML in remote sensing\n## Related work and gaps","[{\"question\":\"What problem does the document address in quantum ML for earth observation?\",\"answer\":\"It addresses the lack of methodology for tuning hyperparameters of the quantum part in hybrid quantum classifiers, specifically for land cover classification tasks.\"},{\"question\":\"How does the proposed approach select the quantum configuration?\",\"answer\":\"It proposes a hybrid quantum neural network and uses a strategy to choose the number of qubits to find an efficient balance between model complexity and accuracy.\"},{\"question\":\"What benefits are reported from using the suggested qubit-selection method?\",\"answer\":\"The method achieves better performance with less model complexity than state of the art and standard techniques in a volcanic eruption case study, and it improves resilience to dataset imbalance. It also provides freely available code for reproduction.\"}]",1779224463,13,{"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,"on-quantum-hyperparameters-selection-in-hybrid-classifiers-for-earth-observation-data","",{"@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/on-quantum-hyperparameters-selection-in-hybrid-classifiers-for-earth-observation-data/31228/",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 document address in quantum ML for earth observation?","Question",{"text":73,"@type":74},"It addresses the lack of methodology for tuning hyperparameters of the quantum part in hybrid quantum classifiers, specifically for land cover classification tasks.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the proposed approach select the quantum configuration?",{"text":78,"@type":74},"It proposes a hybrid quantum neural network and uses a strategy to choose the number of qubits to find an efficient balance between model complexity and accuracy.",{"name":80,"@type":71,"acceptedAnswer":81},"What benefits are reported from using the suggested qubit-selection method?",{"text":82,"@type":74},"The method achieves better performance with less model complexity than state of the art and standard techniques in a volcanic eruption case study, and it improves resilience to dataset imbalance. It also provides freely available code for reproduction.","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}]