{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T07:51:05Z","timestamp":1782460265041,"version":"3.54.5"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quantum Mach. Intell."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Quantum machine learning (QML) has gained increasing attention as a potential framework to address certain data analysis challenges in the future. Earth observation (EO) has entered the era of Big Data, where increasingly sophisticated deep learning models often require substantial computational resources for EO data analysis. Motivated by this, we explore the potential of hybrid quantum-classical learning for EO data classification despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module together with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple simplified EO benchmark settings as a proof-of-concept. Additionally, we experimentally investigated the robustness of the proposed model under reduced-sample and class-imbalanced conditions, providing insights into the behavior of hybrid QML models for EO data analysis.<\/jats:p>","DOI":"10.1007\/s42484-026-00415-3","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T07:42:13Z","timestamp":1782459733000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multitask learning for earth observation data classification with hybrid quantum network"],"prefix":"10.1007","volume":"8","author":[{"given":"Fan","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Guggemos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao Xiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"issue":"4","key":"415_CR1","doi-asserted-by":"publisher","first-page":"040321","DOI":"10.1103\/PRXQuantum.2.040321","volume":"2","author":"L Banchi","year":"2021","unstructured":"Banchi L, Pereira J, Pirandola S (2021) Generalization in quantum machine learning: A quantum information standpoint. PRX Quantum 2(4):040321","journal-title":"PRX Quantum"},{"key":"415_CR2","doi-asserted-by":"crossref","unstructured":"Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M, Nemani R (2015) DeepSat: a learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 1\u201310","DOI":"10.1145\/2820783.2820816"},{"key":"415_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Machine Learning 45:5\u201332","journal-title":"Mach Learn"},{"key":"415_CR4","unstructured":"Broughton M, Verdon G, McCourt T, Martinez AJ, Yoo JH, Isakov SV, Massey P, Halavati R, Niu MY, Zlokapa A et al (2020) Tensorflow quantum: a software framework for quantum machine learning. arXiv preprint arXiv:2003.02989"},{"key":"415_CR5","doi-asserted-by":"publisher","first-page":"582","DOI":"10.22331\/q-2021-11-17-582","volume":"5","author":"MC Caro","year":"2021","unstructured":"Caro MC, Gil-Fuster E, Meyer JJ, Eisert J, Sweke R (2021) Encoding-dependent generalization bounds for parametrized quantum circuits. Quantum 5:582","journal-title":"Quantum"},{"issue":"1","key":"415_CR6","doi-asserted-by":"publisher","first-page":"4919","DOI":"10.1038\/s41467-022-32550-3","volume":"13","author":"MC Caro","year":"2022","unstructured":"Caro MC, Huang H-Y, Cerezo M, Sharma K, Sornborger A, Cincio L, Coles PJ (2022) Generalization in quantum machine learning from few training data. Nature Communications 13(1):4919","journal-title":"Nat Commun"},{"issue":"1","key":"415_CR7","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.1038\/s41467-023-39381-w","volume":"14","author":"MC Caro","year":"2023","unstructured":"Caro MC, Huang H-Y, Ezzell N, Gibbs J, Sornborger AT, Cincio L, Coles PJ, Holmes Z (2023) Out-of-distribution generalization for learning quantum dynamics. Nature Coummunications 14(1):3751","journal-title":"Nat Commun"},{"issue":"8","key":"415_CR8","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1109\/LGRS.2019.2947783","volume":"17","author":"M Carvalho","year":"2019","unstructured":"Carvalho M, Le Saux B, Trouv\u00e9-Peloux P, Champagnat F, Almansa A (2019) Multitask learning of height and semantics from aerial images. IEEE Geoscience and Remote Sensing Letters 17(8):1391\u20131395","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"415_CR9","doi-asserted-by":"crossref","unstructured":"De Falco F, Ceschini A, Sebastianelli A, Le Saux B, Panella M (2024) Quantum latent diffusion models. Quantum Machine Intelligence 6(2):85","DOI":"10.1007\/s42484-024-00224-6"},{"key":"415_CR10","doi-asserted-by":"crossref","unstructured":"Delilbasic A, Le Saux B, Riedel M, Michielsen K, Cavallaro G (2023) A single-step multiclass SVM based on quantum annealing for remote sensing data classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","DOI":"10.1109\/JSTARS.2023.3336926"},{"issue":"14","key":"415_CR11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.131.140601","volume":"131","author":"Y Du","year":"2023","unstructured":"Du Y, Yang Y, Tao D, Hsieh M-H (2023) Problem-dependent power of quantum neural networks on multiclass classification. Physical Review Letters 131(14):140601","journal-title":"Phys Rev Lett"},{"key":"415_CR12","doi-asserted-by":"crossref","unstructured":"Fan F, Shi Y, Guggemos T, Zhu XX (2023) Hybrid quantum-classical convolutional neural network model for image classification. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2023.3312170"},{"key":"415_CR13","doi-asserted-by":"crossref","unstructured":"Fan F, Shi Y, Guggemos T, Zhu XX (2025) Hybrid quantum deep learning with superpixel encoding for earth observation data classification. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.21203\/rs.3.rs-8765173\/v1"},{"issue":"1","key":"415_CR14","doi-asserted-by":"publisher","first-page":"013241","DOI":"10.1103\/PhysRevResearch.6.013241","volume":"6","author":"J Gibbs","year":"2024","unstructured":"Gibbs J, Holmes Z, Caro MC, Ezzell N, Huang H-Y, Cincio L, Sornborger AT, Coles PJ (2024) Dynamical simulation via quantum machine learning with provable generalization. Physical Review Research 6(1):013241","journal-title":"Phys Rev Res"},{"key":"415_CR15","doi-asserted-by":"publisher","first-page":"893","DOI":"10.22331\/q-2023-01-13-893","volume":"7","author":"C Gyurik","year":"2023","unstructured":"Gyurik C, Dunjko V et al (2023) Structural risk minimization for quantum linear classifiers. Quantum 7:893","journal-title":"Quantum"},{"issue":"7","key":"415_CR16","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","volume":"12","author":"P Helber","year":"2019","unstructured":"Helber P, Bischke B, Dengel A, Borth D (2019) EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(7):2217\u20132226","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"415_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"415_CR18","unstructured":"Howard AG (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"415_CR19","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"415_CR20","doi-asserted-by":"crossref","unstructured":"Kapishnikov A, Venugopalan S, Avci B, Wedin B, Terry M, Bolukbasi T (2021) Guided integrated gradients: an adaptive path method for removing noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5050\u20135058","DOI":"10.1109\/CVPR46437.2021.00501"},{"key":"415_CR21","doi-asserted-by":"crossref","unstructured":"Khanal B, Rivas P, Sanjel A, Sooksatra K, Quevedo E, Rodriguez A (2024) Generalization error bound for quantum machine learning in NISQ era-a survey. arXiv preprint arXiv:2409.07626","DOI":"10.1007\/s42484-024-00204-w"},{"key":"415_CR22","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"1","key":"415_CR23","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TGRS.2012.2200043","volume":"51","author":"JM Leiva-Murillo","year":"2012","unstructured":"Leiva-Murillo JM, G\u00f3mez-Chova L, Camps-Valls G (2012) Multitask remote sensing data classification. IEEE Transactions on Geoscience and Remote Sensing 51(1):151\u2013161","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"415_CR24","doi-asserted-by":"crossref","unstructured":"Li X, Sayer AM, Carroll IT, Huang X, Wang J (2024) MT-HCCAR: multi-task deep learning with hierarchical classification and attention-based regression for cloud property retrieval. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp 3\u201318","DOI":"10.1007\/978-3-031-70381-2_1"},{"issue":"6","key":"415_CR25","doi-asserted-by":"publisher","first-page":"5085","DOI":"10.1109\/TGRS.2020.3018879","volume":"59","author":"S Liu","year":"2020","unstructured":"Liu S, Shi Q, Zhang L (2020) Few-shot hyperspectral image classification with unknown classes using multitask deep learning. IEEE Transactions on Geoscience and Remote Sensing 59(6):5085\u20135102","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"415_CR26","doi-asserted-by":"crossref","unstructured":"Liu T, Li J, Lu F, Tang M, Yang G (2024) MLCNet: multi-task level-specific constraint network for building change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","DOI":"10.1109\/JSTARS.2024.3415171"},{"key":"415_CR27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.22331\/q-2020-10-11-341","volume":"4","author":"X-Z Luo","year":"2020","unstructured":"Luo X-Z, Liu J-G, Zhang P, Wang L (2020) Yao. jl: Extensible, efficient framework for quantum algorithm design. Quantum 4:341","journal-title":"Quantum"},{"key":"415_CR28","doi-asserted-by":"crossref","unstructured":"Miller L, Uehara G, Sharma A, Spanias A (2023) Quantum machine learning for optical and SAR classification. In: 2023 24th International Conference on Digital Signal Processing (DSP). IEEE, pp 1\u20135","DOI":"10.1109\/DSP58604.2023.10167979"},{"key":"415_CR29","doi-asserted-by":"crossref","unstructured":"Miroszewski A, Mielczarek J, Czelusta G, Szczepanek F, Grabowski B, Le Saux B, Nalepa J (2023) Detecting clouds in multispectral satellite images using quantum-kernel support vector machines. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","DOI":"10.1109\/JSTARS.2023.3304122"},{"key":"415_CR30","unstructured":"Miroszewski A, Nalepa J, Saux BL, Mielczarek J (2023) Quantum machine learning for remote sensing: exploring potential and challenges. arXiv preprint arXiv:2311.07626"},{"key":"415_CR31","unstructured":"Papa L, Sebastianelli A, Meoni G, Amerini I (2024) On the impact of key design aspects in simulated hybrid quantum neural networks for earth observation. arXiv preprint arXiv:2410.08677"},{"key":"415_CR32","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.isprsjprs.2019.05.004","volume":"154","author":"C Qiu","year":"2019","unstructured":"Qiu C, Mou L, Schmitt M, Zhu XX (2019) Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network. ISPRS Journal of Photogrammetry and Remote Sensing\u00a0154:151\u2013162","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"415_CR33","first-page":"1","volume":"19","author":"C Qiu","year":"2020","unstructured":"Qiu C, Liebel L, Hughes LH, Schmitt M, K\u00f6rner M, Zhu XX (2020) Multitask learning for human settlement extent regression and local climate zone classification. IEEE Geoscience and Remote Sensing Letters 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"415_CR34","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41534-022-00672-7","volume":"9","author":"J Qi","year":"2023","unstructured":"Qi J, Yang C-HH, Chen P-Y, Hsieh M-H (2023) Theoretical error performance analysis for variational quantum circuit based functional regression. npj Quantum Information 9(1):4","journal-title":"NPJ Quant Inf"},{"key":"415_CR35","doi-asserted-by":"crossref","unstructured":"Rodriguez-Grasa P, Farzan-Rodriguez R, Novelli G, Ban Y, Sanz M (2024) Satellite image classification with neural quantum kernels. arXiv preprint arXiv:2409.20356","DOI":"10.1088\/2632-2153\/ada86c"},{"key":"415_CR36","doi-asserted-by":"crossref","unstructured":"Sebastianelli A, Mauro F, Ciabatti G, Spiller D, Saux BL, Gamba P, Ullo S (2024) Quanv4EO: Empowering earth observation by means of quanvolutional neural networks. arXiv:2407.17108","DOI":"10.1109\/TGRS.2025.3556335"},{"issue":"4","key":"415_CR37","doi-asserted-by":"publisher","first-page":"045021","DOI":"10.1088\/2058-9565\/acf1c7","volume":"8","author":"R Wang","year":"2023","unstructured":"Wang R, Richerme P, Chen F (2023) A hybrid quantum-classical neural network for learning transferable visual representation. Quantum Science and Technology 8(4):045021","journal-title":"Quant Sci Technol"},{"key":"415_CR38","doi-asserted-by":"crossref","unstructured":"Wang Y, Albrecht CM, Braham NAA, Liu C, Xiong Z, Zhu XX (2024) Decoupling common and unique representations for multimodal self-supervised learning. 18th European Conference on Computer Vision, ECCV. Springer, pp 1\u201319","DOI":"10.1007\/978-3-031-73397-0_17"},{"key":"415_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s43673-021-00030-3","volume":"32","author":"S Wei","year":"2022","unstructured":"Wei S, Chen Y, Zhou Z, Long G (2022) A quantum convolutional neural network on NISQ devices. AAPPS Bulletin 32:1\u201311","journal-title":"AAPPS Bull"},{"key":"415_CR40","doi-asserted-by":"crossref","unstructured":"Zardini E, Delilbasic A, Blanzieri E, Cavallaro G, Pastorello D (2024) Local binary and multiclass SVMs trained on a quantum annealer. arXiv preprint arXiv:2403.08584","DOI":"10.1109\/TQE.2024.3475875"},{"issue":"12","key":"415_CR41","doi-asserted-by":"publisher","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Yang Q (2021) A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering 34(12):5586\u20135609","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"415_CR42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040221","volume":"60","author":"X Zheng","year":"2021","unstructured":"Zheng X, Gong T, Li X, Lu X (2021) Generalized scene classification from small-scale datasets with multitask learning. IEEE Transactions on Geoscience and Remote Sensing 60:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"415_CR43","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","volume":"145","author":"W Zhou","year":"2018","unstructured":"Zhou W, Newsam S, Li C, Shao Z (2018) PatternNet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS Journal of Photogrammetry and Remote Sensing 145:197\u2013209","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"4","key":"415_CR44","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","volume":"5","author":"XX Zhu","year":"2017","unstructured":"Zhu XX, Tuia D, Mou L, Xia G-S, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5(4):8\u201336","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"3","key":"415_CR45","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MGRS.2020.2964708","volume":"8","author":"XX Zhu","year":"2020","unstructured":"Zhu XX, Hu J, Qiu C, Shi Y, Kang J, Mou L, Bagheri H, Haberle M, Hua Y, Huang R, Hughes L, Li H, Sun Y, Zhang G, Han S, Schmitt M, Wang Y (2020) So2Sat LCZ42: a benchmark data set for the classification of global local climate zones [software and data sets]. IEEE Geoscience and Remote Sensing Magazine 8(3):76\u201389. https:\/\/doi.org\/10.1109\/MGRS.2020.2964708","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"4","key":"415_CR46","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/MGRS.2020.3046356","volume":"9","author":"XX Zhu","year":"2021","unstructured":"Zhu XX, Montazeri S, Ali M, Hua Y, Wang Y, Mou L, Shi Y, Xu F, Bamler R (2021) Deep learning meets SAR: concepts, models, pitfalls, and perspectives. IEEE Geoscience and Remote Sensing Magazine 9(4):143\u2013172","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"1","key":"415_CR47","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s13222-024-00464-7","volume":"24","author":"JM Zollner","year":"2024","unstructured":"Zollner JM, Walther P, Werner M (2024) Satellite image representations for quantum classifiers. Datenbank-Spektrum 24(1):33\u201341","journal-title":"Datenbank-Spektrum"}],"container-title":["Quantum Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00415-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42484-026-00415-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00415-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T07:42:52Z","timestamp":1782459772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42484-026-00415-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,26]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["415"],"URL":"https:\/\/doi.org\/10.1007\/s42484-026-00415-3","relation":{},"ISSN":["2524-4906","2524-4914"],"issn-type":[{"value":"2524-4906","type":"print"},{"value":"2524-4914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,26]]},"assertion":[{"value":"2 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study does not involve human participants or animals, and therefore approval from an ethics committee or Institutional Review Board (IRB) was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable. This study does not involve human participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"72"}}