[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123809-en":3,"doc-seo-123809-105":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},123809,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification - Abstract & Methodology","Synthetic Aperture Radar (SAR) data combines high-dimensional structure with complex spatial correlations, creating major obstacles for efficient processing and interpretable results. Traditional algorithms often face limits when data volume and intricacy grow. The study presents a tensor quantum machine learning (QML) framework to improve feature extraction and pattern recognition by leveraging quantum computational advantages alongside tensor-network representational efficiency. Tensor decomposition schemes reduce dimensionality, while tensor-based quantum circuits perform land-cover classification. Simulation-based preliminary results use synthetic amplitude and phase data, with real-world SAR application planned.","On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification  \nSreejit Duttaa, Sigurd Hubera, Gerhard Kriegera  \na Microwaves and Radar Institute (HR), German Aerospace Center (DLR), 82234 Wessling, Germany.  \nAbstract  \nSynthetic Aperture Radar (SAR) data, characterised by its high-dimensionality and complex spatial correlations, poses significant challenges in terms of efficient processing and meaningful interpretation. Classical algorithms, while effective, often struggle with the sheer volume and intricacy of the data. This paper introduces a novel approach employing tensor quantum machine learning (QML) to tackle the intricacies of SAR data. By harnessing the computational advantages of quantum mechanics and the representational efficiency of tensor networks, we try to achieve enhanced feature extraction and pattern recognition. We look at various Tensor decomposition schemes to reduce data dimensionality as well as Tensor based quantum circuits to perform land-cover classification. Preliminary results, based on simulations, demonstrate the potential of our tensor QML framework. For the scope of this research so far, we worked with simulated amplitude and phase data, but we will be applying the same for real world data in the future. This interdisciplinary study not only opens avenues for improved SAR data analysis but also enriches the burgeoning field of quantum machine learning by highlighting its applicability in remote sensing domains.  \n1 Introduction  \nSynthetic Aperture Radar (SAR) has revolutionised the domain of remote sensing, producing high-resolution imagery that remains unaffected by lighting or atmospheric conditions. Its applications span a wide spectrum: from monitoring urban sprawl, assessing flood damages, observing agricultural practices, to tracking deforestation, mapping geological formations, and even conducting planetary exploration[1] . While conventional techniques have made significant strides in interpreting SAR data, the escalating complexity and sheer volume of such data present formidable challenges.  \nMachine learning, a discipline teeming with algorithmsand statistical models that empower systems to autonomously accomplish specific tasks[2], has been instrumental in surmounting several of these challenges. The integration of machine learning into geoscience, climate change research, and environmental monitoring has heralded transformative advances in SAR research[3] .  \nHowever, as the SAR data deluge shows no signs of abating, classical algorithms reach their performance ceilings. Enter quantum computing—a paradigm that synergizes classical information theory, computer science, and quantum physics[4] . Particularly promising is the emergence of Noisy Intermediate-Scale Quantum (NISQ) technology[5], signalling a significant progression towards more formidable quantum technologies.  \nThis paper embarks on an exploration of tensor quantum machine learning (QML) for SAR data processing. Tensor methods, foundational in modern numerical methods used for simulating many-body physics, have found their niche in machine learning[6] . Quantum machine learning models utilising tensor networks, deemed apt for  \nimminent quantum hardware, have sprouted at the intersection of quantum computing and machine learning[7] . These models, as demonstrated in tensor network quantum machine learning studies[8], can leverage tensor network algorithms to compress classical data representing quantum states.  \nHarnessing the computational power of quantum mechanics and the data representation proficiencies of tensor networks, this interdisciplinary foray aspires to not only refine SAR data analysis but also enrich the vibrant tapestry of quantum machine learning research, accentuating its ramifications in remote sensing.  \n2 Methodology  \nIn this study, we embarked on the task of land cover classification using Synthetic Aperture Radar (SAR) data. Thus far, our work has focused on syn","cbCaidVwUqbuPEqe","https://ap.wps.com/l/cbCaidVwUqbuPEqe","pdf",299412,1,6,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data Generation and Characteristics","[{\"question\":\"Why are classical methods challenging for SAR land-cover classification in this work?\",\"answer\":\"SAR imagery is high-dimensional and exhibits complex spatial correlations, and the large scale and intricacy of the data push classical algorithms toward performance ceilings. The paper argues that this motivates alternative modeling approaches.\"},{\"question\":\"What is the core idea of the tensor quantum machine learning approach here?\",\"answer\":\"The method combines quantum machine learning with tensor networks, using tensor decomposition for dimensionality reduction and tensor-based quantum circuits for classification. The goal is to improve feature extraction and pattern recognition.\"},{\"question\":\"What data and decomposition techniques were used so far?\",\"answer\":\"The study uses simulated SAR complex-valued data by generating amplitude and phase components for land-cover classes. It applies tensor decompositions including CP decomposition, Tucker decomposition, and Tensor Train/Matrix Product State decomposition, and it compares against classical CNN baselines and quantum CNNs.\"}]","On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification - Abstract & Methodology | PDF",1785818664,15,{"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},"on-the-potentials-of-tensor-based-quantum-machine-learning-for-sar-land-cover-classification-abstract-methodology","",{"@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/on-the-potentials-of-tensor-based-quantum-machine-learning-for-sar-land-cover-classification-abstract-methodology/123809/",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","2026-08-04",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},"Why are classical methods challenging for SAR land-cover classification in this work?","Question",{"text":75,"@type":76},"SAR imagery is high-dimensional and exhibits complex spatial correlations, and the large scale and intricacy of the data push classical algorithms toward performance ceilings. The paper argues that this motivates alternative modeling approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core idea of the tensor quantum machine learning approach here?",{"text":80,"@type":76},"The method combines quantum machine learning with tensor networks, using tensor decomposition for dimensionality reduction and tensor-based quantum circuits for classification. The goal is to improve feature extraction and pattern recognition.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and decomposition techniques were used so far?",{"text":84,"@type":76},"The study uses simulated SAR complex-valued data by generating amplitude and phase components for land-cover classes. It applies tensor decompositions including CP decomposition, Tucker decomposition, and Tensor Train/Matrix Product State decomposition, and it compares against classical CNN baselines and quantum CNNs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]