[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122086-en":3,"doc-seo-122086-105":30,"detail-sidebar-cat-0-en-105":90},{"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":20,"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},122086,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","QUANTUM MACHINE LEARNING FOR REMOTE SENSING - EXPLORING POTENTIAL AND CHALLENGES - Research study","The industry of quantum technologies is rapidly expanding, and Quantum Machine Learning (QML) is highlighted for its potential to transform data processing and analysis. This paper examines how QML can be applied to remote sensing, clarifying beliefs about “quantum advantage” for space-derived data. It also identifies open challenges and studies kernel value concentration, which can degrade quantum runtime. Results indicate the issue harms performance but does not completely eliminate potential advantage in QML for remote sensing.","QUANTUM MACHINE LEARNING FOR REMOTE SENSING: EXPLORING POTENTIAL AND CHALLENGES  \nArtur Miroszewski 1, Jakub Nalepa2 ,3, Bertrand Le Saux4, Jakub Mielczarek 1  \n1Institute of Theoretical Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Cracow, Poland  \n2 KP Labs, Bojkowska 37J, 44-100 Gliwice, Poland  \n3 Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland  \n4European Space Agency Φ-lab, Largo Galileo Galilei 1, 00044 Frascati, Italy  \nABSTRACT  \nThe industry of quantum technologies is rapidly expanding, offering promising opportunities for various scientific domains. Among these emerging technologies, Quantum Machine Learning (QML) has attracted considerable attention due to its potential to revolutionize data processing and analysis. In this paper, we investigate the application of QML in the field of remote sensing. It is believed that QML can provide valuable insights for analysis of data from space. We delve into the common beliefs surrounding the quantum advantage in QML for remote sensing and highlight the open challenges that need to be addressed. To shed light on the challenges, we conduct a study focused on the problem of kernel value concentration, a phenomenon that adversely affects the runtime of quantum computers. Our findings indicate that while this issue negatively impacts quantum computer performance, it does not entirely negate the potential quantum advantage in QML for remote sensing.  \nIndex Terms— Quantum Machine Learning, Remote Sensing, Quantum Computation  \n1. INTRODUCTION  \nQuantum computing technologies attract attention from academia, business entities and general public. With the constant development of both technology and theory behind quantum computation, the claims for its potential use grow significantly. At the same time, as those claims get validated, we are becoming more and more aware of the limitations of quantum computing. We review recent developments in the theory of Quantum Machine Learning (QML) and refer them to the subject of remote sensing. We propose the following  \nThis work was funded by the European Space Agency, and supported by the ESA Φ-lab ([https://philab.esa.int/](https://philab.esa.int/)) AI-enhanced Quantum Computing for Earth Observation (QC4EO) initiative, under ESA contract No. 4000137725/22/NL/GLC/my. AM and JM were supported by the Priority Research Areas Anthropocene and Digiworld under the program Excellence Initiative – Research University at the Jagiellonian University in Krakw. JN was supported by the Silesian University of Technology grant for maintaining and developing research potential.  \nunderstanding for quantum advantage in machine learning. We claim quantum advantage if:  \n1. the algorithm run on physical quantum machine solves the machine learning task obtaining better performance than on a classic machine,  \n2. the simulation of the quantum algorithm is not efficient (in terms of cost, energy use, runtime, . . . ) on a classic machine.  \nOne of the industries that might profit in employing quantum technologies for pattern recognition and data analysis is remote sensing. With the constantly increasing amount of data produced in space and specific requirements for data handling, it is natural to look for new technologies which could excel in those tasks. Indeed, scientists are already exploring this topic from the perspective of Quantum Machine Learning [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] . Therefore, we recognize the need for a high-level review of the topic.  \nIn Sec. 2 we introduce the notion of Quantum Machine Learning and discuss potential advantages of exploring this subject. In Sec. 3 we review major challenges for QML to achieve quantum advantage. In Sec. 4 we perform a study on one of those challenges. Sec. 5 concludes the paper.  \n2. QUANTUM MACHINE LEARNING  \nQuantum machine learning refers to the intersection of quantum computing and machine learning. It encompasses the development and application of machine learnin","cbCaiaTIiRZhJSsa","https://ap.wps.com/l/cbCaiaTIiRZhJSsa","pdf",325036,1,4,"English","en",105,"# Introduction\n## Quantum Machine Learning and remote sensing\n# Quantum Machine Learning\n## Definitions and expected sources of quantum advantage\n# Challenges for quantum advantage\n## Kernel value concentration and performance impact\n# Study and results\n# Conclusion","[{\"question\":\"What does the paper claim about the potential of Quantum Machine Learning for remote sensing?\",\"answer\":\"It argues that QML may provide valuable insights for analyzing data from space, while also reviewing the evidence and assumptions behind the idea of quantum advantage.\"},{\"question\":\"How does the paper define “quantum advantage” in this context?\",\"answer\":\"It requires that an algorithm run on a physical quantum machine solves the learning task with better performance than a classical machine, and that classic simulation of the quantum algorithm is not efficient in terms of cost, energy, or runtime.\"},{\"question\":\"What challenge does the study focus on, and what effect is observed?\",\"answer\":\"The study focuses on kernel value concentration, which adversely affects quantum runtime. The findings show it negatively impacts quantum computer performance, but does not entirely negate the potential quantum advantage in remote sensing QML.\"}]","QUANTUM MACHINE LEARNING FOR REMOTE SENSING - EXPLORING POTENTIAL AND CHALLENGES - Research study | PDF",1785808750,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"quantum-machine-learning-for-remote-sensing-exploring-potential-and-challenges-research-study","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/quantum-machine-learning-for-remote-sensing-exploring-potential-and-challenges-research-study/122086/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the paper claim about the potential of Quantum Machine Learning for remote sensing?","Question",{"text":74,"@type":75},"It argues that QML may provide valuable insights for analyzing data from space, while also reviewing the evidence and assumptions behind the idea of quantum advantage.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper define “quantum advantage” in this context?",{"text":79,"@type":75},"It requires that an algorithm run on a physical quantum machine solves the learning task with better performance than a classical machine, and that classic simulation of the quantum algorithm is not efficient in terms of cost, energy, or runtime.",{"name":81,"@type":72,"acceptedAnswer":82},"What challenge does the study focus on, and what effect is observed?",{"text":83,"@type":75},"The study focuses on kernel value concentration, which adversely affects quantum runtime. The findings show it negatively impacts quantum computer performance, but does not entirely negate the potential quantum advantage in remote sensing QML.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"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":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]