{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T13:58:37Z","timestamp":1775656717973,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quantum Mach. Intell."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s42484-026-00384-7","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T13:12:08Z","timestamp":1775653928000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advantage with quantum reservoir computing in medical insurance and health data analysis"],"prefix":"10.1007","volume":"8","author":[{"given":"Muhsin","family":"Tamturk","sequence":"first","affiliation":[]},{"given":"Eran","family":"Ginossar","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Carenzo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"issue":"6","key":"384_CR1","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1038\/s43588-021-00084-1","volume":"1","author":"A Abbas","year":"2021","unstructured":"Abbas A, Sutter D, Zoufal C, Lucchi A, Figalli A, Woerner S (2021) The power of quantum neural networks. Nature Computat Sci 1(6):403\u2013409","journal-title":"Nature Computat Sci"},{"key":"384_CR2","doi-asserted-by":"crossref","unstructured":"Beaulieu D, Kornjaca M, Krunic Z, Stivaktakis M, Ehmer T, Wang S-T, Pham A (2024) Robust quantum reservoir computing for molecular property prediction. arXiv preprint arXiv:2412.06758","DOI":"10.1021\/acs.jcim.5c00958"},{"issue":"3","key":"384_CR3","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","volume":"54","author":"C Bent\u00e9jac","year":"2021","unstructured":"Bent\u00e9jac C, Cs\u00f6rg\u0151 A, Mart\u00ednez-Mu\u00f1oz G (2021) A comparative analysis of gradient boosting algorithms. Artif Intell Rev 54(3):1937\u20131967","journal-title":"Artif Intell Rev"},{"key":"384_CR4","first-page":"1","volume":"2","author":"A Blance","year":"2021","unstructured":"Blance A, Spannowsky M (2021) Quantum machine learning for particle physics using a variational quantum classifier. J High Energy Phys 2:1\u201320","journal-title":"J High Energy Phys"},{"issue":"9","key":"384_CR5","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo M, Arrasmith A, Babbush R, Benjamin SC, Endo S, Fujii K, McClean JR, Mitarai K, Yuan X, Cincio L et al (2021) Variational quantum algorithms. Nat Rev Phys 3(9):625\u2013644","journal-title":"Nat Rev Phys"},{"key":"384_CR6","doi-asserted-by":"crossref","unstructured":"Chen SY-C (2024) Efficient quantum recurrent reinforcement learning via quantum reservoir computing. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 13186\u201313190","DOI":"10.1109\/ICASSP48485.2024.10446089"},{"issue":"15","key":"384_CR7","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.scib.2018.06.007","volume":"63","author":"Z-Y Chen","year":"2018","unstructured":"Chen Z-Y, Zhou Q, Xue C, Yang X, Guo G-C, Guo G-P (2018) 64-qubit quantum circuit simulation. Sci Bull 63(15):964\u2013971","journal-title":"Sci Bull"},{"issue":"3","key":"384_CR8","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1103\/RevModPhys.68.733","volume":"68","author":"A Ekert","year":"1996","unstructured":"Ekert A, Jozsa R (1996a) Quantum computation and shor\u2019s factoring algorithm. Rev Mod Phys 68(3):733","journal-title":"Rev Mod Phys"},{"issue":"3","key":"384_CR9","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1103\/RevModPhys.68.733","volume":"68","author":"A Ekert","year":"1996","unstructured":"Ekert A, Jozsa R (1996b) Quantum computation and shor\u2019s factoring algorithm. Rev Mod Phys 68(3):733","journal-title":"Rev Mod Phys"},{"key":"384_CR10","unstructured":"Farhi E, Goldstone J, Gutmann S (2014) A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028"},{"key":"384_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TQE.2022.3213474","volume":"3","author":"M Grossi","year":"2022","unstructured":"Grossi M, Ibrahim N, Radescu V, Loredo R, Voigt K, Von Altrock C, Rudnik A (2022) Mixed quantum-classical method for fraud detection with quantum feature selection. IEEE Trans Quant Eng 3:1\u201312","journal-title":"IEEE Trans Quant Eng"},{"key":"384_CR12","unstructured":"IBM (2025) IBM Quantum Roadmap. https:\/\/www.ibm.com\/roadmaps\/quantum\/. Accessed 07 Aug 2025"},{"issue":"11","key":"384_CR13","first-page":"738","volume":"11","author":"AA Ibrahim","year":"2020","unstructured":"Ibrahim AA, Ridwan RL, Muhammed MM, Abdulaziz RO, Saheed GA (2020) Comparison of the catboost classifier with other machine learning methods. Int J Adv Comput Sci Appl 11(11):738\u2013748","journal-title":"Int J Adv Comput Sci Appl"},{"key":"384_CR14","unstructured":"Kaggle (2017) Insurance Medical Data. https:\/\/www.kaggle.com\/datasets\/mirichoi0218\/insurance\/data. Accessed 07 Aug 2025"},{"key":"384_CR15","unstructured":"Kaggle (2025) Kaggle. https:\/\/www.kaggle.com. Accessed 07 Aug 2025"},{"issue":"4","key":"384_CR16","doi-asserted-by":"publisher","first-page":"040502","DOI":"10.1088\/2058-9565\/ab4346","volume":"4","author":"P Knight","year":"2019","unstructured":"Knight P, Walmsley I (2019) Uk national quantum technology programme. Quant Sci Technol 4(4):040502","journal-title":"Quant Sci Technol"},{"issue":"9","key":"384_CR17","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1038\/nphys3029","volume":"10","author":"S Lloyd","year":"2014","unstructured":"Lloyd S, Mohseni M, Rebentrost P (2014) Quantum principal component analysis. Nat Phys 10(9):631\u2013633","journal-title":"Nat Phys"},{"issue":"1","key":"384_CR18","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s41534-023-00682-z","volume":"9","author":"P Mujal","year":"2023","unstructured":"Mujal P, Mart\u00ednez-Pe\u00f1a R, Giorgi GL, Soriano MC, Zambrini R (2023a) Time-series quantum reservoir computing with weak and projective measurements. npj Quant Inf 9(1):16","journal-title":"npj Quant Inf"},{"issue":"1","key":"384_CR19","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s41534-023-00682-z","volume":"9","author":"P Mujal","year":"2023","unstructured":"Mujal P, Mart\u00ednez-Pe\u00f1a R, Giorgi GL, Soriano MC, Zambrini R (2023b) Time-series quantum reservoir computing with weak and projective measurements. npj Quant Inf 9(1):16","journal-title":"npj Quant Inf"},{"key":"384_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1687-6","volume-title":"Reservoir Computing","author":"K Nakajima","year":"2021","unstructured":"Nakajima K, Fischer I (2021) Reservoir Computing. Springer, Singapore"},{"issue":"7","key":"384_CR21","doi-asserted-by":"publisher","first-page":"992","DOI":"10.3390\/e25070992","volume":"25","author":"S Raubitzek","year":"2023","unstructured":"Raubitzek S, Mallinger K (2023) On the applicability of quantum machine learning. Entropy 25(7):992","journal-title":"Entropy"},{"issue":"13","key":"384_CR22","doi-asserted-by":"publisher","first-page":"130503","DOI":"10.1103\/PhysRevLett.113.130503","volume":"113","author":"P Rebentrost","year":"2014","unstructured":"Rebentrost P, Mohseni M, Lloyd S (2014) Quantum support vector machine for big data classification. Phys Rev Lett 113(13):130503","journal-title":"Phys Rev Lett"},{"key":"384_CR23","unstructured":"Scikit-Learn (2025) Diabetes Data. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.datasets.load_diabetes.html. Accessed 07 Aug 2025"},{"issue":"8","key":"384_CR24","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3390\/computers13080191","volume":"13","author":"DRIM Setiadi","year":"2024","unstructured":"Setiadi DRIM, Susanto A, Nugroho K, Muslikh AR, Ojugo AA, Gan H-S (2024) Rice yield forecasting using hybrid quantum deep learning model. Computers 13(8):191","journal-title":"Computers"},{"issue":"10","key":"384_CR25","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1080\/03461238.2024.2365390","volume":"2024","author":"B So","year":"2024","unstructured":"So B (2024) Enhanced gradient boosting for zero-inflated insurance claims and comparative analysis of catboost, xgboost, and lightgbm. Scand Actuar J 2024(10):1013\u20131035","journal-title":"Scand Actuar J"},{"key":"384_CR26","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1016\/j.enbuild.2017.11.039","volume":"158","author":"S Touzani","year":"2018","unstructured":"Touzani S, Granderson J, Fernandes S (2018) Gradient boosting machine for modeling the energy consumption of commercial buildings. Energ Build 158:1533\u20131543","journal-title":"Energ Build"},{"key":"384_CR27","doi-asserted-by":"crossref","unstructured":"Walsh C (2023) The past, present, and future of US Government investment in quantum information science: https:\/\/www.quantum.gov. Springer","DOI":"10.1557\/s43577-023-00609-1"},{"key":"384_CR28","doi-asserted-by":"crossref","unstructured":"Wang J, Liu C, Shu X, Jiang H, Yu X, Wang J, Wang W (2019) Network intrusion detection based on xgboost model improved by quantum-behaved particle swarm optimization. In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC), pp 1879\u20131884","DOI":"10.1109\/iSPEC48194.2019.8975295"},{"key":"384_CR29","unstructured":"Wudarski F, OConnor D, Geaney S, Asanjan A.A, Wilson M, Strbac E, Lott PA, Venturelli D (2023) Hybrid quantum-classical reservoir computing for simulating chaotic systems. arXiv preprint arXiv:2311.14105"},{"issue":"20","key":"384_CR30","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1016\/j.scib.2023.08.040","volume":"68","author":"W Xia","year":"2023","unstructured":"Xia W, Zou J, Qiu X, Chen F, Zhu B, Li C, Deng D-L, Li X (2023) Configured quantum reservoir computing for multi-task machine learning. Sci Bull 68(20):2321\u20132329","journal-title":"Sci Bull"},{"issue":"11","key":"384_CR31","doi-asserted-by":"publisher","first-page":"110502","DOI":"10.1103\/PhysRevLett.126.110502","volume":"126","author":"T Xin","year":"2021","unstructured":"Xin T, Che L, Xi C, Singh A, Nie X, Li J, Dong Y, Lu D (2021) Experimental quantum principal component analysis via parametrized quantum circuits. Phys Rev Lett 126(11):110502","journal-title":"Phys Rev Lett"}],"container-title":["Quantum Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00384-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42484-026-00384-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00384-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T13:12:19Z","timestamp":1775653939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42484-026-00384-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["384"],"URL":"https:\/\/doi.org\/10.1007\/s42484-026-00384-7","relation":{},"ISSN":["2524-4906","2524-4914"],"issn-type":[{"value":"2524-4906","type":"print"},{"value":"2524-4914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]},"assertion":[{"value":"13 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}}],"article-number":"41"}}