EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 370 (2026) EPJ Web Conf., 370 (2026) 01021 References
Open Access
Issue
EPJ Web Conf.
Volume 370, 2026
International Conference on Advanced Physics: Innovations for a Sustainable Future (IEMPHYS-26)
Article Number 01021
Number of page(s) 14
DOI https://doi.org/10.1051/epjconf/202637001021
Published online 29 May 2026
  1. A. Andreou, C. X. Mavromoustakis, E. K. Markakis, A. Bourdena, and G. Mastorakis, ”Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing, " IEEE Access, vol. 13, pp. 54622-54635, 2025a, doi: 10.1109/access.2025.3554024. [Google Scholar]
  2. V. Silva, “Advanced algorithms: Unstructured search and integer factorization with Grover and shor, ” Quantum Computing by Practice, pp. 313-337, Dec. 2023. doi:10.1007/978-1-4842-9991-3_10 [Google Scholar]
  3. A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, ”Adversarial Attacks and Defences: A Survey, ” arXiv:1810.00069 [cs, stat], 2018. [Online]. Available: https://arxiv.org/abs/1810.00069 [Google Scholar]
  4. S. R. Sihare, “Quantum voice-based cyber-attacks: Threats, vulnerabilities, and postquantum mitigation strategies, ” SECURITY AND PRIVACY, vol. 8, no. 6, Sep. 2025. doi:10.1002/spy2.70106 [Google Scholar]
  5. J. C. Costa, T. Roxo, H. Proenca and Pedro, “How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses, ” IEEE Access, vol. 12, pp. 1-1, 2024, doi: 10.1109/access.2024.3395118. [CrossRef] [Google Scholar]
  6. H. Eren, O. Karaduman, and M. T. Genc, og」u, "Security and Privacy in the Internet of Everything (IoE): A Review on Blockchain, Edge Computing, AI, and Quantum-Resilient Solutions, " Applied Sciences, vol. 15, no. 15, p. 8704, 2025, doi: 10.3390/app15158704. [Google Scholar]
  7. N. Franco, A. Sakhnenko, L. Stolpmann, D. Thuerck, F. Petsch, A. Rull, and J. M. Lorenz, "Predominant Aspects on Security for Quantum Machine Learn-ing: Literature Review, " in Proc. IEEE Int. Conf. Quantum Computing and Engineering (QCE), 2024, pp. 1467-1477, doi: 10.1109/qce60285.2024.00173. [Google Scholar]
  8. A. Kehoe, P. Wittek, Y. Xue, and A. Pozas-Kerstjens, "Defence Against Adversarial Attacks Using Classical and Quantum-Enhanced Boltzmann Machines, " Machine Learning: Science and Technology, 2021, doi: 10.1088/2632-2153/abf834. [Google Scholar]
  9. M. SaberiKamarposhti, K.-W. Ng, F.-F. Chua, J. Abdullah, M. Yadollahi, M. Moradi, and S. Ahmadpour, "Post-Quantum Healthcare: A Roadmap for Cybersecurity Resilience in Medical Data, ” Heliyon, vol. 10, no. 10, p. e31406, 2024, doi: 10.1016/j.heliyon.2024.e31406. [Google Scholar]
  10. M. Ozdag, ”Adversarial Attacks and Defenses Against Deep Neural Networks: A Survey, ” Procedia Computer Science, vol. 140, pp. 152-161, 2018, doi: 10.1016/j.procs.2018.10.315. [Google Scholar]
  11. R. Alluhaibi, ”Results, ” International Journal of Safety & Security, 2025. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope [Google Scholar]
  12. H. Ren, T. Huang, and H. Yan, ”Adversarial Examples: Attacks and Defenses in the Physical World, ” International Journal of Machine Learning and Cybernetics, vol. 2, no. 5, 2021, doi: 10.1007/s13042-020-01242-z. [Google Scholar]
  13. K. Ren, T. Zheng, Z. Qin, and X. Liu, ”Adversarial Attacks and Defenses in Deep Learning, ” Engineering, vol. 6, no. 3, 2020, doi: 10.1016/j.eng.2019.12.012. [Google Scholar]
  14. S. Anasuri, ”Adversarial Attacks and Defenses in Deep Neural Networks, ” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, 2022, doi: 10.63282/3050-9262.ijaidsml-v3i4p109. [Google Scholar]
  15. L. Schwinn, D. Dobre, S. Gu nnemann, and G. Gidel, “Adversarial Attacks and Defenses in Large Language Models: Old and New Threats, ” Proceedings of Machine Learning Research (PMLR), pp. 103-117, 2023. [Online]. Available: https://proceedings.mlr.press/v239/schwinn23a.html [Google Scholar]
  16. F. Ullah, N. Mohammad, L. Mostarda, D. Cacciagrano, and Y. Zhao, ”Q-P2FL: Quantum-Enhanced Federated Edge Intelligence for Privacy-Preserving Adversarial Attack Detection on Consumer Edge Devices, ” IEEE Transactions on Consumer Electronics, vol. 17, no. 2, pp. 1-1, 2025, doi: 10.1109/tce.2025.3571352. [Google Scholar]
  17. M. T. West, S.-L. Tsang, J. S. Low, C. D. Hill, C. Leckie, L. C. L. Hollenberg, S. M. Erfani, and M. Usman, ”Towards Quantum Enhanced Adversarial Robustness in Machine Learning, ” Nature Machine Intelligence, vol. 5, no. 6, pp. 581-589, 2023, doi: 10.1038/s42256-023-00661-1. [Google Scholar]
  18. Y. Shamoo, ”Adversarial Attacks and Defense Mechanisms in the Age of Quantum Computing, ” Advances in Information Security, Privacy, and Ethics Book Series, vol. 3, pp. 301-344, 2024, doi: 10.4018/979-8-3373-1102-9.ch010. [Google Scholar]
  19. X. Yuan, P. He, Q. Zhu, and X. Li, ”Adversarial Examples: Attacks and Defenses for Deep Learning, ” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2805-2824, 2019, doi: 10.1109/tnnls.2018.2886017. [Google Scholar]
  20. S. Zhou, C. Liu, D. Ye, T. Zhu, W. Zhou, and P. S. Yu, ”Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity, ” ACM Computing Surveys, vol. 55, no. 8, 2022, doi: 10.1145/3547330. [Google Scholar]
  21. J. Yedalla, “Quantum-safe cryptography: Navigating the future of Cybersecurity in the post-quantum era, ” International Journal of Science and Research (IJSR), vol. 14, no. 2, pp. 249-253, Feb. 2025. doi:10.21275/sr25203214146 [Google Scholar]
  22. D. T and P. M, “Quantum-enhanced adaptive defense system for network threat detection, ” 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), pp. 1-6, Aug. 2025. doi:10.1109/nmitcon65824.2025.11187517 [Google Scholar]
  23. H. Muto, “PWRANOVA: Power analysis of flexible ANOVA designs and related tests, ” CRAN: Contributed Packages, Oct. 2025. doi:10.32614/cran.package.pwranova [Google Scholar]
  24. S. A. E’mari, Y. Sanjalawe, and B. A. Allehyani, “Quantum Computing Implications in generative AI cybersecurity, ” Advances in Computational Intelligence and Robotics, pp. 609-642, May 2025. doi:10.4018/979-8-3373-0832-6.ch025 [Google Scholar]
  25. R. Rietsche et al., “Quantum computing, " Electronic Markets, vol. 32, no. 4, pp. 25252536, Aug. 2022. doi:10.1007/s12525-022-00570-y [Google Scholar]
  26. S. Mishra, O. Agarwal, and S. K. Patel, “Quantum decryption using Shor’s algorithm, ” 2024 IEEE Pune Section International Conference (PuneCon), pp. 1-6, Dec. 2024. doi:10.1109/punecon63413.2024.10895777 [Google Scholar]
  27. J. Cui et al., “Pet image denoising using unsupervised deep learning, ” European Journal of Nuclear Medicine and Molecular Imaging, vol. 46, no. 13, pp. 2780-2789, Aug. 2019. doi:10.1007/s00259-019-04468-4 [Google Scholar]
  28. W. Wang et al., “Exploiting student parallelism for low-latency GPU inference of Bert-like models in online services, ” Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, pp. 3055-3066, Aug. 2025. doi:10.1145/3711896.3736949 [Google Scholar]
  29. A. Babu, S. G. Ghatnekar, A. Saxena, and D. Mandal, “Entanglement-enabled quantum kernels for enhanced feature mapping, ” APL Quantum, vol. 2, no. 1, Feb. 2025. doi:10.1063/5.0240894 [Google Scholar]
  30. H. Cowlessur, C. Thapa, T. Alpcan, and S. Camtepe, “A hybrid quantum neural network for split learning, ” Quantum Machine Intelligence, vol. 7, no. 2, Aug. 2025. doi:10.1007/s42484-025-00295-z [Google Scholar]
  31. H. Singh, “Future directions and challenges, quantum supremacy, and beyond, ” Quantum Technology Applications, Impact, and Future Challenges, pp. 141-162, Jan. 2025. doi:10.1201/9781003537243-9 [Google Scholar]
  32. L. Hour, M. Go, and Y. Han, “Improving Zero-noise extrapolation for quantum-gate error mitigation using a noise-aware folding method, ” IEEE Access, vol. 14, pp. 3736237372, 2026. doi:10.1109/access.2026.3672056 [Google Scholar]
  33. M. Suhaib, V. Karthick, and M. D, A novel approach for mitigating gate-level noise in IBM quantum hardware via Zero noise extrapolation (ZNE) and probabilistic error cancellation (PEC) techniques, Dec. 2025. doi:10.21203/rs.3.rs-7883122/v1 [Google Scholar]
  34. C. R. Giardina, “Noisy Intermediate Scale Quantum NISQ computing, ” Probability for Deep Learning Quantum, pp. 247-257, 2025. doi:10.1016/b978-0-443-24834-4.00013-8 [Google Scholar]
  35. M. Chenu, “Quantum emulators: CPU, single GPU and multiple gpus performance comparison, ” Procedia Computer Science, vol. 267, pp. 218-226, 2025. doi:10.1016/j.procs.2025.08.248 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.