{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T04:28:02Z","timestamp":1764563282521,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819549566"},{"type":"electronic","value":"9789819549573"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4957-3_8","type":"book-chapter","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T04:24:01Z","timestamp":1764563041000},"page":"87-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum Patches for\u00a0Efficient Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4100-5217","authenticated-orcid":false,"given":"Ban Q.","family":"Tran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5771-7470","authenticated-orcid":false,"given":"Chuong K.","family":"Luong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0722-1941","authenticated-orcid":false,"given":"Susan","family":"Mengel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","DOI":"10.1038\/nature14539"},{"key":"8_CR2","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)"},{"issue":"1","key":"8_CR3","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Choe, J., Shim, H.: Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp.\u00a02219\u20132228 (2019)","DOI":"10.1109\/CVPR.2019.00232"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Yun, S., et al.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp.\u00a06023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"8_CR6","unstructured":"Uddin, A., et\u00a0al.: Saliencymix: a saliency guided data augmentation strategy for better regularization. arXiv preprint arXiv:2006.01791 (2020)"},{"key":"8_CR7","unstructured":"Nielsen, M.A., Chuang, I.L.: Quantum computation and quantum information. Cambridge university press (2010)"},{"issue":"9","key":"8_CR8","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo, M.: Variational quantum algorithms. Nat. Rev. Phys. 3(9), 625\u2013644 (2021)","journal-title":"Nat. Rev. Phys."},{"issue":"1","key":"8_CR9","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."},{"issue":"7671","key":"8_CR10","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195\u2013202 (2017)","journal-title":"Nature"},{"key":"8_CR11","unstructured":"Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 (2014)"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.tcs.2014.05.025","volume":"560","author":"CH Bennett","year":"2014","unstructured":"Bennett, C.H., Brassard, G.: Quantum cryptography: public key distribution and coin tossing. Theoret. Comput. Sci. 560, 7\u201311 (2014)","journal-title":"Theoret. Comput. Sci."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","volume":"2","author":"J Preskill","year":"2018","unstructured":"Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)","journal-title":"Quantum"},{"key":"8_CR14","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019)","journal-title":"J. Big Data"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"27829","DOI":"10.1109\/ACCESS.2024.3367806","volume":"12","author":"G Swathi","year":"2024","unstructured":"Swathi, G., Altalbe, A., Kumar, R.P.: Qucnet: quantum-inspired convolutional neural networks for optimized thyroid nodule classification. IEEE Access 12, 27829\u201327842 (2024)","journal-title":"IEEE Access"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Gordienko, Y., Trochun, Y., Taran, V., Khmelnytskyi, A., Stirenko, S.: HNN-QC n: Hybrid neural network with multiple backbones and quantum transformation as data augmentation technique. AI 6(2), 36 (2025)","DOI":"10.3390\/ai6020036"},{"key":"8_CR18","unstructured":"Baglio, J.: Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies. arXiv preprint arXiv:2405.04401 (2024)"},{"issue":"4","key":"8_CR19","first-page":"6107","volume":"45","author":"CJ Rani","year":"2023","unstructured":"Rani, C.J., Devarakonda, N.: Generative adversarial network based data augmentation and quantum based convolution neural network for the classification of Indian classical dance forms. J. Intell. Fuzzy Syst. 45(4), 6107\u20136125 (2023)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"1","key":"8_CR20","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/s42005-024-01552-6","volume":"7","author":"M Hibat-Allah","year":"2024","unstructured":"Hibat-Allah, M., Mauri, M., Carrasquilla, J., Perdomo-Ortiz, A.: A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models. Commun. Phys. 7(1), 68 (2024)","journal-title":"Commun. Phys."},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, W., He, Y., Li, H.: Deep learning model for short-term photovoltaic power prediction based on data augmentation and quantum computing. In: 2024 China Automation Congress (CAC), pp.\u00a04097\u20134102. IEEE (2024)","DOI":"10.1109\/CAC63892.2024.10864724"},{"key":"8_CR22","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"8_CR23","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"issue":"6","key":"8_CR24","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1038\/s41567-018-0124-x","volume":"14","author":"S Boixo","year":"2018","unstructured":"Boixo, S.: Characterizing quantum supremacy in near-term devices. Nat. Phys. 14(6), 595\u2013600 (2018)","journal-title":"Nat. Phys."},{"issue":"10","key":"8_CR25","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TCSVT.2018.2870832","volume":"29","author":"R Cong","year":"2018","unstructured":"Cong, R.: Review of visual saliency detection with comprehensive information. IEEE Trans. Circuits Syst. Video Technol. 29(10), 2941\u20132959 (2018)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a0770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR27","unstructured":"Bergholm, V., et\u00a0al.: Pennylane: automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968 (2018)"},{"key":"8_CR28","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"2","key":"8_CR29","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1103\/PhysRev.104.483","volume":"104","author":"CE Porter","year":"1956","unstructured":"Porter, C.E., Thomas, R.G.: Fluctuations of nuclear reaction widths. Phys. Rev. 104(2), 483 (1956)","journal-title":"Phys. Rev."}],"container-title":["Lecture Notes in Computer Science","Multi-disciplinary Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4957-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T04:24:09Z","timestamp":1764563049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4957-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9789819549566","9789819549573"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4957-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,12,2]]},"assertion":[{"value":"2 December 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MIWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miwai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miwai25.miwai.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}