{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:22:50Z","timestamp":1770754970785,"version":"3.50.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031258909","type":"print"},{"value":"9783031258916","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25891-6_26","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T14:03:34Z","timestamp":1678370614000},"page":"341-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Variational Algorithm for Quantum Single Layer Perceptron"],"prefix":"10.1007","author":[{"given":"Antonio","family":"Macaluso","sequence":"first","affiliation":[]},{"given":"Filippo","family":"Orazi","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Klusch","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Lodi","sequence":"additional","affiliation":[]},{"given":"Claudio","family":"Sartori","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer, New York (2001). https:\/\/doi.org\/10.1007\/978-0-387-21606-5","DOI":"10.1007\/978-0-387-21606-5"},{"issue":"5","key":"26_CR2","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H., et al.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw."},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Macaluso, A., Clissa, L., Lodi, S., Sartori, C.: Quantum splines for non-linear approximations. In: Proceedings of the 17th ACM International Conference on Computing Frontiers, pp. 249\u2013252 (2020)","DOI":"10.1145\/3387902.3394032"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)","DOI":"10.1103\/PhysRevLett.103.150502"},{"issue":"4","key":"26_CR5","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/nphys3272","volume":"11","author":"S Aaronson","year":"2015","unstructured":"Aaronson, S.: Read the fine print. Nat. Phys. 11(4), 291 (2015)","journal-title":"Nat. Phys."},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","DOI":"10.1038\/323533a0"},{"issue":"6","key":"26_CR7","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.: The power of quantum neural networks. Nat. Comput. Sci. 1(6), 403\u2013409 (2021)","journal-title":"Nat. Comput. Sci."},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Tacchino, F., Macchiavello, C., Gerace, D., Bajoni, D.: An artificial neuron implemented on an actual quantum processor, zak1998quantum. NPJ Quantum Inf. 5(1), 26 (2019)","DOI":"10.1038\/s41534-019-0140-4"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Grant, E., et al.: Hierarchical quantum classifiers. NPJ Quantum Inf. 4(1), 1\u20138 (2018)","DOI":"10.1038\/s41534-018-0116-9"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Huggins, W., Patil, P., Mitchell, B., Whaley, K.B., Stoudenmire, E.M.: Towards quantum machine learning with tensor networks. Quantum Sci. Technol. 4(2), 024001 (2019)","DOI":"10.1088\/2058-9565\/aaea94"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Machine learning by unitary tensor network of hierarchical tree structure. New J. Phys. 21(7), 073059 (2019)","DOI":"10.1088\/1367-2630\/ab31ef"},{"key":"26_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/978-3-030-50433-5_45","volume-title":"Computational Science \u2013 ICCS 2020","author":"A Macaluso","year":"2020","unstructured":"Macaluso, A., Clissa, L., Lodi, S., Sartori, C.: A variational algorithm for quantum neural networks. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12142, pp. 591\u2013604. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50433-5_45"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4(4), 043001 (2019)","DOI":"10.1088\/2058-9565\/ab4eb5"},{"key":"26_CR14","unstructured":"Havlicek, V., et al.: Supervised learning with quantum enhanced feature spaces. Nature (2018)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Smolin, J.A., DiVincenzo, D.P.: Five two-bit quantum gates are sufficient to implement the quantum Fredkin gate. Phys. Rev. A 53(4), 2855 (1996)","DOI":"10.1103\/PhysRevA.53.2855"},{"key":"26_CR16","unstructured":"Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Circuit-centric quantum classifiers. arXiv preprint arXiv:1804.00633 (2018)"},{"key":"26_CR17","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Judd, J.S.: Neural Network Design and the Complexity of Learning. MIT Press, Cambridge (1990)","DOI":"10.7551\/mitpress\/4932.001.0001"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Havl\u00ed\u010dek, V., et al.: Supervised learning with quantum-enhanced feature spaces. Nature 567(7747), 209\u2013212 (2019)","DOI":"10.1038\/s41586-019-0980-2"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Shende, V.V., Prasad, A.K., Markov, I.L., Hayes, J.P.: Synthesis of quantum logic circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (2006)","DOI":"10.1109\/TCAD.2005.855930"},{"key":"26_CR21","unstructured":"Mottonen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. arXiv preprint quant-ph\/0407010 (2004)"},{"key":"26_CR22","unstructured":"Goto, T., Tran, Q.H., Nakajima, K.: Universal approximation property of quantum feature map. arXiv preprint arXiv:2009.00298 (2020)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25891-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:20:42Z","timestamp":1680690042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25891-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258909","9783031258916"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25891-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}