{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:45:43Z","timestamp":1777873543257,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736972","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T20:52:41Z","timestamp":1754254361000},"page":"2210-2221","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fully Quanvolutional Networks for Time Series Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5251-2137","authenticated-orcid":false,"given":"Nabil Anan","family":"Orka","sequence":"first","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-5698","authenticated-orcid":false,"given":"Ehtashamul","family":"Haque","sequence":"additional","affiliation":[{"name":"BRAC University, Dhaka, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3028-4932","authenticated-orcid":false,"given":"Md. Abdul","family":"Awal","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"Charles Sturt University, Orange, Australia and Washington University of Science and Technology, Alexandria, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08449-y"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-39355-6_10"},{"key":"e_1_3_2_2_3_1","volume-title":"The UEA Multivariate Time Series Classification Archive, 2018","author":"Anthony","year":"1811","unstructured":"Anthony Bagnall et al. 2018. The UEA Multivariate Time Series Classification Archive, 2018. arxiv: 1811.00075 Retrieved from https:\/\/arxiv.org\/abs\/1811.00075."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/525960"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3475"},{"key":"e_1_3_2_2_6_1","first-page":"238","volume-title":"Nature","volume":"624","author":"Castelvecchi Davide","year":"2023","unstructured":"Davide Castelvecchi. 2023. IBM Releases First-Ever 1,000-Qubit Quantum Chip. Nature, Vol. 624, 7991 (2023), 238-238."},{"key":"e_1_3_2_2_7_1","volume-title":"High Performance Convolutional Neural Networks for Document Processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, Guy Lorette (Ed.). Universit\u00e9 de Rennes 1","author":"Chellapilla Kumar","year":"2006","unstructured":"Kumar Chellapilla, Sidd Puri, and Patrice Simard. 2006. High Performance Convolutional Neural Networks for Document Processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, Guy Lorette (Ed.). Universit\u00e9 de Rennes 1, Suvisoft, La Baule (France)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41567-019-0648-8"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.92.012327"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhad154"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"e_1_3_2_2_13_1","first-page":"8790","article-title":"Taking Advantage of Noise in Quantum Reservoir","volume":"13","author":"Domingo Laia","year":"2023","unstructured":"Laia Domingo, G Carlo, and F Borondo. 2023. Taking Advantage of Noise in Quantum Reservoir Computing. Sci. Rep., Vol. 13, 1 (2023), 8790.","journal-title":"Computing. Sci. Rep."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.3.023153"},{"key":"e_1_3_2_2_15_1","volume-title":"Probability and Statistics: The science of Uncertainty","author":"Evans Michael J","unstructured":"Michael J Evans and Jeffrey S Rosenthal. 2004. Probability and Statistics: The science of Uncertainty. Macmillan, United Kingdom."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_17_1","first-page":"2","article-title":"Quanvolutional Neural Networks","volume":"2","author":"Henderson Maxwell","year":"2020","unstructured":"Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, and Tristan Cook. 2020. Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits. Quantum Mach. Intell., Vol. 2, 1 (2020), 2.","journal-title":"Powering Image Recognition with Quantum Circuits. Quantum Mach. Intell."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42484-021-00061-x"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-022-03442-8"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126843"},{"key":"e_1_3_2_2_23_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2017","unstructured":"Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arxiv: 1412.6980 Retrieved from https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_3_2_2_24_1","volume-title":"Weinberger (Eds.)","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F. Pereira, C.J. Burges, L. Bottou, and K.Q. Weinberger (Eds.), Vol. 25. Curran Associates, Inc., Lake Tahoe, USA."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_26_1","first-page":"187","article-title":"Detection and Identification of Power Quality Disturbance Signals in New Power System Based on Quantum Classic Hybrid Convolutional Neural Networks. In International Conference on Data Security and Privacy Protection. Springer, Xi'an","author":"Yue Li","year":"2024","unstructured":"Yue Li et al. 2024. Detection and Identification of Power Quality Disturbance Signals in New Power System Based on Quantum Classic Hybrid Convolutional Neural Networks. In International Conference on Data Security and Privacy Protection. Springer, Xi'an, China, 187-203.","journal-title":"China"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.114.140504"},{"key":"e_1_3_2_2_28_1","first-page":"1","volume-title":"ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. In The Twelfth International Conference on Learning Representations.","author":"Luo Donghao","year":"2024","unstructured":"Donghao Luo and Xue Wang. 2024. ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. In The Twelfth International Conference on Learning Representations., Vienna, Austria, 1-43."},{"key":"e_1_3_2_2_29_1","volume-title":"Garnett (Eds.)","volume":"29","author":"Luo Wenjie","year":"2016","unstructured":"Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. 2016. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc., Barcelona, Spain."},{"key":"e_1_3_2_2_30_1","unstructured":"Denny Mattern Darya Martyniuk Henri Willems Fabian Bergmann and Adrian Paschke. 2021. Variational Quanvolutional Neural Networks with Enhanced Image Encoding. arxiv: 2106.07327 Retrieved from https:\/\/arxiv.org\/abs\/2106.07327."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.103"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1972505"},{"key":"e_1_3_2_2_33_1","unstructured":"Adam Paszke et al. 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems H. Wallach H. Larochelle A. Beygelzimer F. dtextquotesingle Alch\u00e9-Buc E. Fox and R. Garnett (Eds.) Vol. 32. Curran Associates Inc. Vancouver Canada."},{"key":"e_1_3_2_2_34_1","first-page":"100619","article-title":"Systematic Literature Review","volume":"51","author":"Peral-Garc\u00eda David","year":"2024","unstructured":"David Peral-Garc\u00eda, Juan Cruz-Benito, and Francisco Jos\u00e9 Garc\u00eda-Pe nalvo. 2024. Systematic Literature Review: Quantum Machine Learning and Its Applications. Comput. Sci. Rev., Vol. 51 (2024), 100619.","journal-title":"Quantum Machine Learning and Its Applications. Comput. Sci. Rev."},{"key":"e_1_3_2_2_35_1","volume-title":"Classification of Hybrid Quantum-Classical Computing. In International Conference on Computational Science. Springer, Prague, Czech Republic, 18-33","author":"Phillipson Frank","year":"2023","unstructured":"Frank Phillipson, Niels Neumann, and Robert Wezeman. 2023. Classification of Hybrid Quantum-Classical Computing. In International Conference on Computational Science. Springer, Prague, Czech Republic, 18-33."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3338145"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.130503"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4937.001.0001"},{"key":"e_1_3_2_2_39_1","first-page":"17","volume-title":"Mexican International Conference on Artificial Intelligence. Springer","author":"Rivera-Ruiz Mayra Alejandra","year":"2023","unstructured":"Mayra Alejandra Rivera-Ruiz, Sandra Leticia Ju\u00e1rez-Osorio, Andres Mendez-Vazquez, Jos\u00e9 Mauricio L\u00f3pez-Romero, and Eduardo Rodriguez-Tello. 2023. 1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification. In Mexican International Conference on Artificial Intelligence. Springer, Yucat\u00e1n, Mexico, 17-35."},{"key":"e_1_3_2_2_40_1","first-page":"397","volume-title":"GQNN: Greedy Quanvolutional Neural Network Model. In International Conference on Image Processing and Capsule Networks. Springer","author":"Savla Aansh","year":"2022","unstructured":"Aansh Savla, Ali Abbas Kanadia, Deep Mehta, and Kriti Srivastava. 2022. GQNN: Greedy Quanvolutional Neural Network Model. In International Conference on Image Processing and Capsule Networks. Springer, Bangkok, Thailand, 397-410."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96424-9"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.5220\/0012271600003636"},{"key":"e_1_3_2_2_43_1","first-page":"1","volume-title":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE","author":"Sridevi S","year":"2022","unstructured":"S Sridevi, T Kanimozhi, K Issac, and M Sudha. 2022. Quanvolution Neural Network to Recognize Arrhythmia from 2D Scalogram Features of ECG Signals. In 2022 International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE, Kottayam, India, 1-5."},{"key":"e_1_3_2_2_44_1","first-page":"1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE","author":"Christian","unstructured":"Christian Szegedy et al. 2015. Going Deeper with Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Boston, USA, 1-9."},{"key":"e_1_3_2_2_45_1","first-page":"044010","article-title":"Quantum Implementation of an Artificial Feed-Forward Neural Network. Quantum Sci","volume":"5","author":"Tacchino Francesco","year":"2020","unstructured":"Francesco Tacchino, Panagiotis Barkoutsos, Chiara Macchiavello, Ivano Tavernelli, Dario Gerace, and Daniele Bajoni. 2020. Quantum Implementation of an Artificial Feed-Forward Neural Network. Quantum Sci. Technol., Vol. 5, 4 (2020), 044010.","journal-title":"Technol."},{"key":"e_1_3_2_2_46_1","volume-title":"An Artificial Neuron Implemented on an Actual Quantum Processor. npj Quantum Inf","author":"Tacchino Francesco","year":"2019","unstructured":"Francesco Tacchino, Chiara Macchiavello, Dario Gerace, and Daniele Bajoni. 2019. An Artificial Neuron Implemented on an Actual Quantum Processor. npj Quantum Inf., Vol. 5, 1 (2019), 26."},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, California, USA, 6105-6114."},{"key":"e_1_3_2_2_48_1","first-page":"1","article-title":"Omni-Scale CNNs: A Simple and Effective Kernel Size Configuration for Time Series Classification. In The Tenth International Conference on Learning Representations","author":"Tang Wensi","year":"2022","unstructured":"Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, and Jing Jiang. 2022. Omni-Scale CNNs: A Simple and Effective Kernel Size Configuration for Time Series Classification. In The Tenth International Conference on Learning Representations., Virtual, 1-17.","journal-title":"Virtual"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3353461"},{"key":"e_1_3_2_2_50_1","volume-title":"The Eleventh International Conference on Learning Representations., Kigali, Rwanda, 1-23","author":"Wu Haixu","year":"2023","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2023. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations., Kigali, Rwanda, 1-23."},{"key":"e_1_3_2_2_51_1","first-page":"6523","article-title":"Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. In 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Toronto","author":"Huck Yang Chao-Han","year":"2021","unstructured":"Chao-Han Huck Yang et al. 2021. Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. In 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Toronto, Canada, 6523-6527.","journal-title":"Canada"},{"key":"e_1_3_2_2_52_1","unstructured":"Fisher Yu and Vladlen Koltun. 2016. Multi-Scale Context Aggregation by Dilated Convolutions. arxiv: 1511.07122 Retrieved from https:\/\/arxiv.org\/abs\/1511.07122."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3442091"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736972","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:02:03Z","timestamp":1777572123000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":53,"alternative-id":["10.1145\/3711896.3736972","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736972","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}