{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:51:43Z","timestamp":1782489103832,"version":"3.54.5"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032299178","type":"print"},{"value":"9783032299185","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-29918-5_3","type":"book-chapter","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:26:28Z","timestamp":1782487588000},"page":"33-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Quantum Machine Learning for\u00a0Assessing the\u00a0Resilience of\u00a0Post-quantum Cryptography"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-101X","authenticated-orcid":false,"given":"Jaroslaw A.","family":"Miszczak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"3_CR1","unstructured":"OpenSSH 10. Release Notes (2025). https:\/\/www.openssh.com\/txt\/release-10.0"},{"key":"3_CR2","unstructured":"Post-quantum cryptography standardization (2025). https:\/\/csrc.nist.gov\/pqc-standardization"},{"key":"3_CR3","unstructured":"pqcrypto (v.0.3.4): Post-quantum cryptography for Python (2025). https:\/\/pypi.org\/project\/pqcrypto\/"},{"key":"3_CR4","unstructured":"Qiskit machine learning (2025). https:\/\/github.com\/qiskit-community\/qiskit-machine-learning"},{"key":"3_CR5","unstructured":"Reference Implementation of version 24 of the Java SE Platform (2025). https:\/\/openjdk.org\/projects\/jdk\/24\/"},{"key":"3_CR6","unstructured":"Reference Implementation of version 26 of the Java SE Platform (2026). https:\/\/openjdk.org\/projects\/jdk\/26\/"},{"issue":"7779","key":"3_CR7","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1038\/s41586-019-1666-5","volume":"574","author":"F Arute","year":"2019","unstructured":"Arute, F., et al.: Quantum supremacy using a programmable superconducting processor. Nature 574(7779), 505\u2013510 (2019). https:\/\/doi.org\/10.1038\/s41586-019-1666-5","journal-title":"Nature"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Baksi, A., Breier, J., Dasu, V.A., Hou, X., Kim, H., Seo, H.: New results on machine learning-based distinguishers. IEEE Access 11, 54175\u201354187 (2023). https:\/\/doi.org\/10.1109\/access.2023.3270396","DOI":"10.1109\/access.2023.3270396"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Benedetti, M., Buhrman, H., Weggemans, J.: Complement sampling: provable, verifiable and nisqable quantum advantage in sample complexity (2025). https:\/\/doi.org\/10.48550\/ARXIV.2502.08721","DOI":"10.48550\/ARXIV.2502.08721"},{"issue":"4","key":"3_CR10","doi-asserted-by":"publisher","first-page":"043023","DOI":"10.1088\/1367-2630\/ab14b5","volume":"21","author":"M Benedetti","year":"2019","unstructured":"Benedetti, M., Grant, E., Wossnig, L., Severini, S.: Adversarial quantum circuit learning for pure state approximation. New J. Phys. 21(4), 043023 (2019). https:\/\/doi.org\/10.1088\/1367-2630\/ab14b5","journal-title":"New J. Phys."},{"key":"3_CR11","volume-title":"Post-Quantum Cryptography","year":"2009","unstructured":"Bernstein, D.J., Buchmann, J., Dahmen, E. (eds.): Post-Quantum Cryptography. Springer, Heidelberg (2009)"},{"issue":"7671","key":"3_CR12","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1038\/nature23461","volume":"549","author":"DJ Bernstein","year":"2017","unstructured":"Bernstein, D.J., Lange, T.: Post-quantum cryptography. Nature 549(7671), 188\u2013194 (2017). https:\/\/doi.org\/10.1038\/nature23461","journal-title":"Nature"},{"issue":"9","key":"3_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo, M., et al.: Variational quantum algorithms. Nat. Rev. Phys. 3(9), 625\u2013644 (2021). https:\/\/doi.org\/10.1038\/s42254-021-00348-9","journal-title":"Nat. Rev. Phys."},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Chen, L.: International Conference on Research in Security Standardisation, pp. 3\u201313. Springer (2025). https:\/\/doi.org\/10.1007\/978-3-031-87541-0_1. Standardisation of and Migration to Post-Quantum Cryptography","DOI":"10.1007\/978-3-031-87541-0_1"},{"issue":"1","key":"3_CR15","doi-asserted-by":"publisher","first-page":"012324","DOI":"10.1103\/physreva.98.012324","volume":"98","author":"PL Dallaire-Demers","year":"2018","unstructured":"Dallaire-Demers, P.L., Killoran, N.: Quantum generative adversarial networks. Phys. Rev. A 98(1), 012324 (2018). https:\/\/doi.org\/10.1103\/physreva.98.012324","journal-title":"Phys. Rev. A"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Dubrova, E., Ngo, K., G\u00e4rtner, J., Wang, R.: Breaking a fifth-order masked implementation of crystals-kyber by copy-paste. In: Proceedings of the 10th ACM Asia Public-Key Cryptography Workshop, ASIA CCS 2023, pp. 10\u201320. ACM (2023). https:\/\/doi.org\/10.1145\/3591866.3593072","DOI":"10.1145\/3591866.3593072"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks (2014). https:\/\/doi.org\/10.48550\/arXiv.1406.2661","DOI":"10.48550\/arXiv.1406.2661"},{"key":"3_CR18","doi-asserted-by":"publisher","unstructured":"Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th annual ACM symposium on Theory of computing, pp. 212\u2013219 (1996).https:\/\/doi.org\/10.1145\/237814.237866","DOI":"10.1145\/237814.237866"},{"issue":"8","key":"3_CR19","doi-asserted-by":"publisher","first-page":"086002","DOI":"10.1088\/1402-4896\/ad5ed1","volume":"99","author":"S Hariharasitaraman","year":"2024","unstructured":"Hariharasitaraman, S., Mishra, N., Vishnuvardhanan, D.: QHopNN: investigating quantum advantage in cryptanalysis using a quantum hopfield neural network. Phys. Scr. 99(8), 086002 (2024). https:\/\/doi.org\/10.1088\/1402-4896\/ad5ed1","journal-title":"Phys. Scr."},{"issue":"7747","key":"3_CR20","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","volume":"567","author":"V Havl\u00ed\u010dek","year":"2019","unstructured":"Havl\u00ed\u010dek, V., C\u00f3rcoles, A.D., Temme, K., Harrow, A.W., Kandala, A., Chow, J.M., Gambetta, J.M.: Supervised learning with quantum-enhanced feature spaces. Nature 567(7747), 209\u2013212 (2019). https:\/\/doi.org\/10.1038\/s41586-019-0980-2","journal-title":"Nature"},{"issue":"6598","key":"3_CR21","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1126\/science.abn7293","volume":"376","author":"HY Huang","year":"2022","unstructured":"Huang, H.Y., et al.: Quantum advantage in learning from experiments. Science 376(6598), 1182\u20131186 (2022). https:\/\/doi.org\/10.1126\/science.abn7293","journal-title":"Science"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Huang, H.Y., et al.: Power of data in quantum machine learning. Nat. Commun. 12(1) (2021). https:\/\/doi.org\/10.1038\/s41467-021-22539-9","DOI":"10.1038\/s41467-021-22539-9"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Islam, M., Turkeli, S., Ozaydin, F.: A survey of quantum generative adversarial networks: architectures, use cases, and real-world implementations (2025). https:\/\/doi.org\/10.48550\/arXiv.2506.18002","DOI":"10.48550\/arXiv.2506.18002"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Kannwischer, M.J., Schwabe, P., Stebila, D., Wiggers, T.: Improving software quality in cryptography standardization projects. In: IEEE European Symposium on Security and Privacy, EuroS&P 2022 - Workshops, Genoa, Italy, 6\u201310 June 2022, pp. 19\u201330. IEEE Computer Society, Los Alamitos, CA, USA (2022). https:\/\/doi.org\/10.1109\/EuroSPW55150.2022.00010","DOI":"10.1109\/EuroSPW55150.2022.00010"},{"key":"3_CR25","unstructured":"Kim, H., Lim, S., Baksi, A., Kim, D., Yoon, S., Jang, K., Seo, H.: Quantum artificial intelligence on cryptanalysis. Cryptology ePrint Archive (2023). https:\/\/ia.cr\/2023\/004"},{"key":"3_CR26","unstructured":"Kinga, D., Adam, J.B., et\u00a0al.: A method for stochastic optimization. In: International conference on learning representations (ICLR). vol.\u00a05. San Diego, California (2015). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Mohammad, K.: Cyber shield: advances in detection, isolation, and containment mechanisms. In: American Institute of Aeronautics and Astronautics (2025)","DOI":"10.2514\/6.2025-2724"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Mosca, M.: Cybersecurity in an era with quantum computers: will we be ready? IEEE Secur. Priv. 16(5), 38\u201341 (2018)","DOI":"10.1109\/MSP.2018.3761723"},{"key":"3_CR29","doi-asserted-by":"publisher","unstructured":"Ngo, T.A., Nguyen, T., Thang, T.C.: A survey of recent advances in quantum generative adversarial networks. Electronics 12(4) (2023).https:\/\/doi.org\/10.3390\/electronics12040856","DOI":"10.3390\/electronics12040856"},{"key":"3_CR30","doi-asserted-by":"publisher","unstructured":"Nguyen, V.L., Nguyen, L.H., Hwang, R.H., Canberk, B., Duong, T.Q.: Quantum machine learning for 6G network intelligence and adversarial threats. IEEE Commun. Stand. Mag. 9 (2025). https:\/\/doi.org\/10.1109\/MCOMSTD.2025.3575261","DOI":"10.1109\/MCOMSTD.2025.3575261"},{"key":"3_CR31","doi-asserted-by":"publisher","unstructured":"Nokhwal, S., Nokhwal, S., Pahune, S., Chaudhary, A.: Quantum generative adversarial networks: Bridging classical and quantum realms. In: 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI), pp. 105\u2013109. ISMSI 2024, ACM (2024). https:\/\/doi.org\/10.1145\/3665065.3665082","DOI":"10.1145\/3665065.3665082"},{"issue":"23","key":"3_CR32","doi-asserted-by":"publisher","first-page":"3852","DOI":"10.3390\/math12233852","volume":"12","author":"M Pajuhanfard","year":"2024","unstructured":"Pajuhanfard, M., Kiani, R., Sheng, V.S.: Survey of quantum generative adversarial networks (qgan) to generate images. Mathematics 12(23), 3852 (2024). https:\/\/doi.org\/10.3390\/math12233852","journal-title":"Mathematics"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Prasad, R., Koren, A.: Safeguarding 6G: Security and Privacy for the Next Generation, River Publishers (2025)","DOI":"10.1201\/9788770047951"},{"key":"3_CR34","unstructured":"Red Hat Inc.: 4 key steps to prepare for post-quantum cryptography (2025). https:\/\/www.redhat.com\/en\/resources\/4-steps-for-postquantum-cryptography-checklist"},{"key":"3_CR35","doi-asserted-by":"publisher","unstructured":"Romero, J., Aspuru-Guzik, A.: Variational quantum generators: generative adversarial quantum machine learning for continuous distributions (2019). https:\/\/doi.org\/10.48550\/arXiv.1901.00848","DOI":"10.48550\/arXiv.1901.00848"},{"key":"3_CR36","unstructured":"Sahin, M.E., et al.: Qiskit machine learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators (2025). https:\/\/arXiv.org\/abs\/2505.17756"},{"issue":"2","key":"3_CR37","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1137\/s0036144598347011","volume":"41","author":"PW Shor","year":"1999","unstructured":"Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41(2), 303\u2013332 (1999). https:\/\/doi.org\/10.1137\/s0036144598347011","journal-title":"SIAM Rev."},{"issue":"12","key":"3_CR38","doi-asserted-by":"publisher","first-page":"1900070","DOI":"10.1002\/qute.201900070","volume":"2","author":"S Sim","year":"2019","unstructured":"Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quant. Technol. 2(12), 1900070 (2019)","journal-title":"Adv. Quant. Technol."},{"key":"3_CR39","doi-asserted-by":"publisher","unstructured":"Ueda, K., Matsuo, A.: Optimizing ansatz design in quantum generative adversarial networks using large language models (2025). https:\/\/doi.org\/10.48550\/arXiv.2503.12884","DOI":"10.48550\/arXiv.2503.12884"},{"key":"3_CR40","doi-asserted-by":"publisher","unstructured":"Zaman, K., Marchisio, A., Hanif, M.A., Shafique, M.: A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead (2023). https:\/\/doi.org\/10.48550\/arXiv.2310.10315","DOI":"10.48550\/arXiv.2310.10315"},{"key":"3_CR41","doi-asserted-by":"publisher","unstructured":"Zoufal, C., Lucchi, A., Woerner, S.: Quantum generative adversarial networks for learning and loading random distributions. npj Quant. Inf. 5(1), (2019). https:\/\/doi.org\/10.1038\/s41534-019-0223-2","DOI":"10.1038\/s41534-019-0223-2"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2026 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29918-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:26:49Z","timestamp":1782487609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29918-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032299178","9783032299185"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29918-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"27 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}