{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T16:27:47Z","timestamp":1743784067801,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030904388"},{"type":"electronic","value":"9783030904395"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-90439-5_47","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:13:49Z","timestamp":1638454429000},"page":"601-612","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Quantum 3D Convolutional Neural Network with Application in Video Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6777-2107","authenticated-orcid":false,"given":"Kostas","family":"Blekos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-1247","authenticated-orcid":false,"given":"Dimitrios","family":"Kosmopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"47_CR1","unstructured":"Adcock, J.,et al.: Advances in quantum machine learning. arXiv:1512.02900 December 2015"},{"key":"47_CR2","doi-asserted-by":"publisher","unstructured":"Allcock, J., Hsieh, C.Y., Kerenidis, I., Zhang, S.: Quantum Algorithms for Feedforward Neural Networks. ACM Trans. Quant. Comput. 1(1), 6:1\u20136:24 (2020). https:\/\/doi.org\/10.1145\/3411466","DOI":"10.1145\/3411466"},{"issue":"1","key":"47_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1093\/nsr\/nwy149","volume":"6","author":"J Allcock","year":"2019","unstructured":"Allcock, J., Zhang, S.: Quantum machine learning. Nat. Sci. Rev. 6(1), 26\u201328 (2019). https:\/\/doi.org\/10.1093\/nsr\/nwy149","journal-title":"Nat. Sci. Rev."},{"issue":"3","key":"47_CR4","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0020-0255(00)00056-6","volume":"128","author":"EC Behrman","year":"2000","unstructured":"Behrman, E.C., Nash, L.R., Steck, J.E., Chandrashekar, V.G., Skinner, S.R.: Simulations of quantum neural networks. Inf. Sci. 128(3), 257\u2013269 (2000). https:\/\/doi.org\/10.1016\/S0020-0255(00)00056-6","journal-title":"Inf. Sci."},{"key":"47_CR5","unstructured":"Cerezo, M., et al.: Variational quantum algorithms. arXiv:2012.09265 (2020)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Chatzis, S.P., Kosmopoulos, D.: A nonparametric bayesian approach toward stacked convolutional independent component analysis. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.321"},{"issue":"12","key":"47_CR7","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","volume":"15","author":"I Cong","year":"2019","unstructured":"Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nat. Phys. 15(12), 1273\u20131278 (2019). https:\/\/doi.org\/10.1038\/s41567-019-0648-8","journal-title":"Nat. Phys."},{"issue":"9","key":"47_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11128-018-2004-9","volume":"17","author":"Y Dang","year":"2018","unstructured":"Dang, Y., Jiang, N., Hu, H., Ji, Z., Zhang, W.: Image classification based on quantum k-nearest-neighbor algorithm. Quantum Inf. Process. 17(9), 1\u201318 (2018). https:\/\/doi.org\/10.1007\/s11128-018-2004-9","journal-title":"Quantum Inf. Process."},{"key":"47_CR9","unstructured":"Garg, S., Ramakrishnan, G.: Advances in quantum deep learning: an overview. arXiv:2005.04316 May 2020"},{"key":"47_CR10","doi-asserted-by":"publisher","unstructured":"Gawron, P., Lewi\u0144ski, S.: Multi-spectral image classification with quantum neural network. In: IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, pp. 3513\u20133516, September 2020. https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9323065","DOI":"10.1109\/IGARSS39084.2020.9323065"},{"issue":"1","key":"47_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42484-020-00012-y","volume":"2","author":"M Henderson","year":"2020","unstructured":"Henderson, M., Shakya, S., Pradhan, S., Cook, T.: Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Mach. Intell. 2(1), 1\u20139 (2020). https:\/\/doi.org\/10.1007\/s42484-020-00012-y","journal-title":"Quantum Mach. Intell."},{"key":"47_CR12","unstructured":"Hern\u00e1ndez, H.I.G., Ruiz, R.T., Sun, G.H.: Image classification via quantum machine learning. arXiv:2011.02831 December 2020"},{"issue":"4","key":"47_CR13","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s11831-018-9269-0","volume":"26","author":"SK Jeswal","year":"2018","unstructured":"Jeswal, S.K., Chakraverty, S.: Recent developments and applications in quantum neural network: a review. Arch. Comput. Methods Eng. 26(4), 793\u2013807 (2018). https:\/\/doi.org\/10.1007\/s11831-018-9269-0","journal-title":"Arch. Comput. Methods Eng."},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014","DOI":"10.1109\/CVPR.2014.223"},{"key":"47_CR15","unstructured":"Kerenidis, I., Landman, J., Prakash, A.: Quantum algorithms for deep convolutional neural networks. In: International Conference on Learning Representations, September 2019"},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Kulkarni, V., Kulkarni, M., Pant, A.: Quantum computing methods for supervised learning. arXiv:2006.12025 June 2020","DOI":"10.1007\/s42484-021-00050-0"},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Lockwood, O., Si, M.: Reinforcement learning with quantum variational circuits. arXiv:2008.07524 August 2020","DOI":"10.1609\/aiide.v16i1.7437"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Materzynska, J., Berger, G., Bax, I., Memisevic, R.: The jester dataset: A large-scale video dataset of human gestures. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2874\u20132882. IEEE Computer Society (2019)","DOI":"10.1109\/ICCVW.2019.00349"},{"key":"47_CR19","doi-asserted-by":"publisher","unstructured":"Nguyen, N.T., Kenyon, G.T.: Image classification using quantum inference on the d-wave 2x. In: 2018 IEEE International Conference on Rebooting Computing (ICRC), pp. 1\u20137, November 2018. https:\/\/doi.org\/10.1109\/ICRC.2018.8638596","DOI":"10.1109\/ICRC.2018.8638596"},{"issue":"2","key":"47_CR20","doi-asserted-by":"publisher","first-page":"025201","DOI":"10.1088\/1612-202X\/abd23c","volume":"18","author":"XF Niu","year":"2021","unstructured":"Niu, X.F., Ma, W.P.: A novel quantum neural network based on multi- level activation function. Laser Phys. Lett. 18(2), 025201 (2021). https:\/\/doi.org\/10.1088\/1612-202X\/abd23c","journal-title":"Laser Phys. Lett."},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Oh, S., Choi, J., Kim, J.: A tutorial on quantum convolutional neural networks (QCNN). arXiv:2009.09423 September 2020","DOI":"10.1109\/ICTC49870.2020.9289439"},{"issue":"3","key":"47_CR22","doi-asserted-by":"publisher","first-page":"030502","DOI":"10.1088\/2058-9565\/aab859","volume":"3","author":"A Perdomo-Ortiz","year":"2018","unstructured":"Perdomo-Ortiz, A., Benedetti, M., Realpe-G\u00f3mez, J., Biswas, R.: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Sci. Technol. 3(3), 030502 (2018). https:\/\/doi.org\/10.1088\/2058-9565\/aab859","journal-title":"Quantum Sci. Technol."},{"issue":"7","key":"47_CR23","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.physleta.2014.11.061","volume":"379","author":"M Schuld","year":"2015","unstructured":"Schuld, M., Sinayskiy, I., Petruccione, F.: Simulating a perceptron on a quantum computer. Phys. Lett. A 379(7), 660\u2013663 (2015). https:\/\/doi.org\/10.1016\/j.physleta.2014.11.061","journal-title":"Phys. Lett. A"},{"issue":"4","key":"47_CR24","doi-asserted-by":"publisher","first-page":"044010","DOI":"10.1088\/2058-9565\/abb8e4","volume":"5","author":"F Tacchino","year":"2020","unstructured":"Tacchino, F., Barkoutsos, P., Macchiavello, C., Tavernelli, I., Gerace, D., Bajoni, D.: Quantum implementation of an artificial feed-forward neural network. Quantum Sci. Technol. 5(4), 044010 (2020). https:\/\/doi.org\/10.1088\/2058-9565\/abb8e4","journal-title":"Quantum Sci. Technol."},{"key":"47_CR25","doi-asserted-by":"publisher","unstructured":"Tacchino, F., Barkoutsos, P.K., Macchiavello, C., Gerace, D., Tavernelli, I., Bajoni, D.: Variational learning for quantum artificial neural networks. In: 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 130\u2013136, October 2020. https:\/\/doi.org\/10.1109\/QCE49297.2020.00026","DOI":"10.1109\/QCE49297.2020.00026"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Feiszli, M.: Video classification with channel-separated convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00565"},{"key":"47_CR27","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2018","unstructured":"Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., Baik, S.W.: Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6, 1155\u20131166 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2017.2778011","journal-title":"IEEE Access"},{"key":"47_CR28","doi-asserted-by":"publisher","unstructured":"Wan, K.H., Dahlsten, O., Kristj\u00e1nsson, H., Gardner, R., Kim, M.S.: Quantum generalisation of feedforward neural networks. npj Quantum Inf. 3(1), 1\u20138 (2017). https:\/\/doi.org\/10.1038\/s41534-017-0032-4","DOI":"10.1038\/s41534-017-0032-4"},{"key":"47_CR29","doi-asserted-by":"publisher","unstructured":"Wu, Z., Wang, X., Jiang, Y.G., Ye, H., Xue, X.: Modeling spatial-temporal clues in a hybrid deep learning framework for video classification. In: Proceedings of the 23rd ACM International Conference on Multimedia, MM 2015, pp. 461\u2013470. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2733373.2806222","DOI":"10.1145\/2733373.2806222"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: Proceedings of the European Conference on Computer Vision (ECCV), September 2018","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"47_CR31","doi-asserted-by":"crossref","unstructured":"Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015","DOI":"10.1109\/CVPR.2015.7299101"},{"issue":"3","key":"47_CR32","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s10773-021-04747-7","volume":"60","author":"N-R Zhou","year":"2021","unstructured":"Zhou, N.-R., Liu, X.-X., Chen, Y.-L., Du, N.-S.: Quantum k-nearest-neighbor image classification algorithm based on K-L transform. Int. J. Theoret. Phys. 60(3), 1209\u20131224 (2021). https:\/\/doi.org\/10.1007\/s10773-021-04747-7","journal-title":"Int. J. Theoret. Phys."},{"issue":"1","key":"47_CR33","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/s10773-009-0183-y","volume":"49","author":"R Zhou","year":"2009","unstructured":"Zhou, R.: Quantum competitive neural network. Int. J. Theoret. Phys. 49(1), 110 (2009). https:\/\/doi.org\/10.1007\/s10773-009-0183-y","journal-title":"Int. J. Theoret. Phys."},{"issue":"12","key":"47_CR34","doi-asserted-by":"publisher","first-page":"3209","DOI":"10.1007\/s10773-007-9437-8","volume":"46","author":"R Zhou","year":"2007","unstructured":"Zhou, R., Ding, Q.: Quantum M-P neural network. Int. J. Theoret. Phys. 46(12), 3209\u20133215 (2007). https:\/\/doi.org\/10.1007\/s10773-007-9437-8","journal-title":"Int. J. Theoret. Phys."}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90439-5_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T23:13:23Z","timestamp":1673910803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90439-5_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030904388","9783030904395"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90439-5_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/?utm_source=researchbib","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":"135","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":"48","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":"36% - 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":"3","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":"2.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}