{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:55:59Z","timestamp":1781794559912,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3787109.3815246","type":"proceedings-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:17:19Z","timestamp":1781792239000},"page":"298-304","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1447-0428","authenticated-orcid":false,"given":"Fang","family":"Qi","sequence":"first","affiliation":[{"name":"Computer Science, Tulane University, New Orleans, LA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3545-286X","authenticated-orcid":false,"given":"Lu","family":"Peng","sequence":"additional","affiliation":[{"name":"Computer Science, Tulane University, New Orleans, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6994-5278","authenticated-orcid":false,"given":"Zhengming","family":"Ding","sequence":"additional","affiliation":[{"name":"Tulane University, NEW ORLEANS, LA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Amira Abbas David Sutter Christa Zoufal Aur\u00e9lien Lucchi Alessio Figalli and Stefan Woerner. 2021. The power of quantum neural networks. Nature Computational Science 1 6 (2021) 403\u2013409.","DOI":"10.1038\/s43588-021-00084-1"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.32782\/cmis\/3392-15"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Marcello Benedetti Erika Lloyd Stefan Sack and Mattia Fiorentini. 2019. Parameterized quantum circuits as machine learning models. Quantum science and technology 4 4 (2019) 043001.","DOI":"10.1088\/2058-9565\/ab4eb5"},{"key":"e_1_3_3_1_5_2","unstructured":"Ville Bergholm Josh Izaac Maria Schuld Christian Gogolin Shahnawaz Ahmed Vishnu Ajith M\u00a0Sohaib Alam Guillermo Alonso-Linaje B AkashNarayanan Ali Asadi et\u00a0al. 2018. Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1811.04968 (2018)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Andrew Blance and Michael Spannowsky. 2021. Quantum machine learning for particle physics using a variational quantum classifier. Journal of High Energy Physics 2021 2 (2021) 1\u201320.","DOI":"10.1007\/JHEP02(2021)212"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Denis Bokhan Alena\u00a0S Mastiukova Aleksey\u00a0S Boev Dmitrii\u00a0N Trubnikov and Aleksey\u00a0K Fedorov. 2022. Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning. Frontiers in Physics 10 (2022) 1069985.","DOI":"10.3389\/fphy.2022.1069985"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Matthias\u00a0C Caro Hsin-Yuan Huang Marco Cerezo Kunal Sharma Andrew Sornborger Lukasz Cincio and Patrick\u00a0J Coles. 2022. Generalization in quantum machine learning from few training data. Nature communications 13 1 (2022) 4919.","DOI":"10.1038\/s41467-022-32550-3"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Marco Cerezo Andrew Arrasmith Ryan Babbush Simon\u00a0C Benjamin Suguru Endo Keisuke Fujii Jarrod\u00a0R McClean Kosuke Mitarai Xiao Yuan Lukasz Cincio et\u00a0al. 2021. Variational quantum algorithms. Nature Reviews Physics 3 9 (2021) 625\u2013644.","DOI":"10.1038\/s42254-021-00348-9"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Avinash Chalumuri Raghavendra Kune and BS Manoj. 2021. A hybrid classical-quantum approach for multi-class classification. Quantum Information Processing 20 3 (2021) 119.","DOI":"10.1007\/s11128-021-03029-9"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Iris Cong Soonwon Choi and Mikhail\u00a0D Lukin. 2019. Quantum convolutional neural networks. Nature Physics 15 12 (2019) 1273\u20131278.","DOI":"10.1038\/s41567-019-0648-8"},{"key":"e_1_3_3_1_12_2","unstructured":"Chao Ding Shi Wang Yaonan Wang and Weibo Gao. 2024. Quantum machine learning for multiclass classification beyond kernel methods. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.02913 (2024)."},{"key":"e_1_3_3_1_13_2","unstructured":"Robert Geirhos Kantharaju Narayanappa Benjamin Mitzkus Tizian Thieringer Matthias Bethge Felix\u00a0A Wichmann and Wieland Brendel. 2021. Partial success in closing the gap between human and machine vision. Advances in Neural Information Processing Systems 34 (2021) 23885\u201323899."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Biraja Ghoshal Allan Tucker Bal Sanghera and Wai Lup\u00a0Wong. 2021. Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection. Computational Intelligence 37 2 (2021) 701\u2013734.","DOI":"10.1111\/coin.12411"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7116-5"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Vojt\u011bch Havl\u00ed\u010dek Antonio\u00a0D C\u00f3rcoles Kristan Temme Aram\u00a0W Harrow Abhinav Kandala Jerry\u00a0M Chow and Jay\u00a0M Gambetta. 2019. Supervised learning with quantum-enhanced feature spaces. Nature 567 7747 (2019) 209\u2013212.","DOI":"10.1038\/s41586-019-0980-2"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Max Henderson Jarred Gallina and Michael Brett. 2021. Methods for accelerating geospatial data processing using quantum computers. Quantum Machine Intelligence 3 1 (2021) 4.","DOI":"10.1007\/s42484-020-00034-6"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Maxwell Henderson Samriddhi Shakya Shashindra Pradhan and Tristan Cook. 2020. Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Machine Intelligence 2 1 (2020) 2.","DOI":"10.1007\/s42484-020-00012-y"},{"key":"e_1_3_3_1_19_2","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1903.12261 (2019)."},{"key":"e_1_3_3_1_20_2","unstructured":"Dan Hendrycks Norman Mu Ekin\u00a0D Cubuk Barret Zoph Justin Gilmer and Balaji Lakshminarayanan. 2019. Augmix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1912.02781 (2019)."},{"key":"e_1_3_3_1_21_2","unstructured":"Katherine Hermann Ting Chen and Simon Kornblith. 2020. The origins and prevalence of texture bias in convolutional neural networks. Advances in Neural Information Processing Systems 33 (2020) 19000\u201319015."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Hsin-Yuan Huang Michael Broughton Masoud Mohseni Ryan Babbush Sergio Boixo Hartmut Neven and Jarrod\u00a0R McClean. 2021. Power of data in quantum machine learning. Nature communications 12 1 (2021) 2631.","DOI":"10.1038\/s41467-021-22539-9"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3676641.3715984"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Danyal Maheshwari Daniel Sierra-Sosa and Begonya Garcia-Zapirain. 2021. Variational quantum classifier for binary classification: Real vs synthetic dataset. IEEE access 10 (2021) 3705\u20133715.","DOI":"10.1109\/ACCESS.2021.3139323"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Sam McArdle Suguru Endo Al\u00e1n Aspuru-Guzik Simon\u00a0C Benjamin and Xiao Yuan. 2020. Quantum computational chemistry. Reviews of Modern Physics 92 1 (2020) 015003.","DOI":"10.1103\/RevModPhys.92.015003"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Sam McArdle Tyson Jones Suguru Endo Ying Li Simon\u00a0C Benjamin and Xiao Yuan. 2019. Variational ansatz-based quantum simulation of imaginary time evolution. npj Quantum Information 5 1 (2019) 75.","DOI":"10.1038\/s41534-019-0187-2"},{"key":"e_1_3_3_1_27_2","unstructured":"Matthias Minderer Josip Djolonga Rob Romijnders Frances Hubis Xiaohua Zhai Neil Houlsby Dustin Tran and Mario Lucic. 2021. Revisiting the calibration of modern neural networks. Advances in neural information processing systems 34 (2021) 15682\u201315694."},{"key":"e_1_3_3_1_28_2","unstructured":"Rafael M\u00fcller Simon Kornblith and Geoffrey\u00a0E Hinton. 2019. When does label smoothing help? Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_3_1_29_2","unstructured":"Lukas Muttenthaler Jonas Dippel Lorenz Linhardt Robert\u00a0A Vandermeulen and Simon Kornblith. 2022. Human alignment of neural network representations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2211.01201 (2022)."},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.5555\/1972505"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Wenhui Ren Weikang Li Shibo Xu Ke Wang Wenjie Jiang Feitong Jin Xuhao Zhu Jiachen Chen Zixuan Song Pengfei Zhang et\u00a0al. 2022. Experimental quantum adversarial learning with programmable superconducting qubits. Nature Computational Science 2 11 (2022) 711\u2013717.","DOI":"10.1038\/s43588-022-00351-9"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Maria Schuld Alex Bocharov Krysta\u00a0M Svore and Nathan Wiebe. 2020. Circuit-centric quantum classifiers. Physical Review A 101 3 (2020) 032308.","DOI":"10.1103\/PhysRevA.101.032308"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_3_1_34_2","unstructured":"Hongyi Zhang Moustapha Cisse Yann\u00a0N Dauphin and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1710.09412 (2017)."},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Hao-kai Zhang Chenghong Zhu Mingrui Jing and Xin Wang. 2023. Statistical analysis of quantum state learning process in quantum neural networks. Advances in Neural Information Processing Systems 36 (2023) 33133\u201333160.","DOI":"10.52202\/075280-1438"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00059"}],"event":{"name":"GLSVLSI '26: Great Lakes Symposium on VLSI 2026","location":"Canandaigua , NY , USA","acronym":"GLSVLSI '26","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the Great Lakes Symposium on VLSI 2026"],"original-title":[],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:33:46Z","timestamp":1781793226000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3787109.3815246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":35,"alternative-id":["10.1145\/3787109.3815246","10.1145\/3787109"],"URL":"https:\/\/doi.org\/10.1145\/3787109.3815246","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}