{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/advantages-and-bottlenecks-of-quantum-machine","title":"Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing","arxiv_id":"2101.10657","date":"2021-01-26","proceeding":null,"authors":["Daniela A. Zaidenberg","Alessandro Sebastianelli","Dario Spiller","Bertrand Le Saux","Silvia Liberata Ullo"],"abstract":"This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.","url_abs":"https://arxiv.org/abs/2101.10657v3","url_pdf":"https://arxiv.org/pdf/2101.10657v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"advantages-and-bottlenecks-of-quantum-machine","repo_url":"https://github.com/ESA-PhiLab/QNN4EO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"quantum-machine-learning","task_name":"Quantum Machine Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}