Dergiler / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 11 Issue: 2

Quantum Computing in 6G Networks: Opportunities and Challenges

Sayfa
—
Yayın Tarihi
2025-08-31
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The paper outlines the potential solutions that quantum computing can provide to meet the performance demands of 6G, and discusses future research directions to further explore this promising technology intersection.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Quantum computing is a technology that utilizes quantum mechanics principles such as superposition and entanglement to offer significant advantages over classical computing.
📊
Temel Bulgular & Veri:With the development of 6G technology, quantum computing's high data processing capacity, efficiency in optimization processes, and contribution to secure communication systems are becoming increasingly important.
💡
Sonuç & Katkı:The paper outlines the potential solutions that quantum computing can provide to meet the performance demands of 6G, and discusses future research directions to further explore this promising technology intersection.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Quantum Computing · Quantum Machine Learning · Quantum Optimization

Abstract

Quantum computing is a technology that utilizes quantum mechanics principles such as superposition and entanglement to offer significant advantages over classical computing. With the development of 6G technology, quantum computing's high data processing capacity, efficiency in optimization processes, and contribution to secure communication systems are becoming increasingly important. This paper examines the role of quantum computing in 6G networks. It discusses its potential contributions in areas such as network optimization, quantum machine learning, secure communication protocols, and fault-tolerant computing. The ultra-low latency, high data rate, and dynamic spectrum management requirements of 6G can be significantly improved by integrating quantum algorithms and hybrid quantum-classical systems. However, obstacles such as current limitations in quantum hardware, error correction requirements, and implementation challenges are also considered in detail. The paper outlines the potential solutions that quantum computing can provide to meet the performance demands of 6G, and discusses future research directions to further explore this promising technology intersection.

🔖

Bu Makaleye Atıf Yap

Osmanca, M. S., Güllü, M., Karhan, D., Çimen, S. (2025). Quantum Computing in 6G Networks: Opportunities and Challenges. Gazi Mühendislik Bilimleri Dergisi, Volume: 11 Issue: 2. https://ajindex.com/articles/1397360

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (139)

  1. S. Glisic and B. Lorenzo, "Quantum computing and neuroscience for 6G/7G networks: Survey," Intelligent Systems with Applications, p. 200346, September 2024.doi:10.1016/j.iswa.2024.200346
  2. M. Belkhir, H. Benkaouha, and E. Benkhelifa, "Quantum vs Classical Computing: A Comparative Analysis," in Proc. 7th Int. Conf. on Fog and Mobile Edge Computing (FMEC), 12-15 December 2022, Paris,France. Available: https://ieeexplore.ieee.org/document/10062753. [Accessed: 14 March 2023].
  3. P. D. Banik and A. Nath, "Past, Present and Future of Quantum Computing: A Systematic Study," Int. J. of Scientific Research in Computer Science, Engineering and Information Technology, vol. 7, no. 2, pp. 56-63, December 2021.doi: 0.32628/CSEIT217694
  4. Sachi Nandan Mohanty, Rajanikanth Aluvalu and Sarita Mohanty, Quantum Computing: Evolution and Applications, John Wiley and Sons 2023.
  5. C. Kaur, V. Mahamuni, and S. Jha, "Emerging Applications and Challenges in Quantum Computing: A Literature Survey," in Proc. IEEE Int. Conf. on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 1-2 August 2024, Port Louis, Mauritius. Available: https://ieeexplore.ieee.org/document/10645271. [Accessed: 29 August 2024].
  6. Z. Yang, M. Zolanvari, and R. Jain, "A Survey of Important Issues in Quantum Computing and Communications," IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 1059–1094, March 2023. doi: 10.1109/comst.2023.3259874
  7. V. Padmavathi, B. Vishnu Vardhan, and A. V. N. Krishna, "Quantum Cryptography and Quantum Key Distribution Protocols: A Survey," in Proc. 6th Int. Advanced Computing Conf. (IACC), 27-28 Feburary 2016, Bhimavaram, India. Available: https://ieeexplore.ieee.org/document/7544898. [Accessed: 18 August 2016].
  8. A. Sharma, and A. Kumar, "A survey on quantum key distribution," International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT, 27-28 September, Ghaziabad, India. Available: https://ieeexplore.ieee.org/abstract/document/8977649. [Accessed: 03 February 2024].
  9. A. Haldorai, A. Ramu, S. Murugan, “Cognitive Radio Communication and Applications for Urban Spaces”. In: Computing and Communication Systems in Urban Development. Urban Computing. Springer, Cham 2019. doi: 10.1007/978-3-030-26013-2_8
  10. M. Aboussalah, C. Chi, and C. G. Lee, "Quantum computing reduces systemic risk in financial networks," Sci Rep, 3990, March 2023. doi: 10.1038/s41598-023-30710-z
  11. S. Gupta and V. Sharma, "Effects of Quantum computing on Businesses," in 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 9-11 May 2023, London, United Kingdom. Available: https://ieeexplore.ieee.org/document/10166880. [Accessed: 4 July 2023].
  12. J. Li, F. Gao, S. Lin, M. Guo, Y. Li, H. Liu,. and Q. Wen, "Quantum k-fold cross-validation for nearest neighbor classification algorithm," Physica A: Statistical Mechanics and its Applications, vol. 611, p. 128435, February 2023. doi: 10.1016/j.physa.2022.128435
  13. A. Saxena, J. Mancilla, I. Montalban and C. Pere, Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making, Birmingham Packt Publishing, 2023.
  14. R. Sotelo, D. Corbelletto, E. Dri, E. Giusto, and B. Montrucchio, "Quantum Computing in Finance: the Intesa Sanpaolo Experience," IEEE Engineering Management Review, vol. 52, no. 1, pp. 9–15, Mar. 2024. doi: 10.1109/EMR.2024.3385518
  15. S. Naik, E. Yeniaras, G. Hellstern, G. Prasad, and S. K. L. P. Vishwakarma, "From portfolio optimization to quantum blockchain and security: A systematic review of quantum computing in finance," Financial Innovation, vol. 11, no. 1, pp. 1–67, Feb. 2025. doi:10.1186/s40854-025-00751-6
  16. G. Mazzola, "Quantum computing for chemistry and physics applications from a Monte Carlo perspective," The Journal of Chemical Physics, vol. 160, no. 1, pp. 010902, Jan. 2024. doi:10.1063/5.0173591
  17. S. Guo, J. Sun, H. Qian, M. Gong, Y. Zhang, F. Chen,. and J. W. Pan, "Experimental quantum computational chemistry with optimized unitary coupled cluster ansatz," Nature Physics, vol. 20, no. 8, pp. 1240–1246, August 2024. doi: 10.1038/s41567-024-02530-z
  18. R. Izsák, C. Riplinger, N. S. Blunt, B. de Souza, N. Holzmann, O. Crawford,. and P. Schopf, "Quantum computing in pharma: A multilayer embedding approach for near future applications," Journal of Computational Chemistry, vol. 44, no. 3, pp. 406–421, Jun 2022. doi: 10.48550/arXiv.2202.04460
  19. A. Pyrkov, A. Aliper, D. Bezrukov, Y.C. Lin, D. Polykovskiy, P. Kamya and A. Zhavoronkov, "Quantum computing for near-term applications in generative chemistry and drug discovery," Drug Discovery Today, vol. 28, no. 8, p. 103675, August 2023. doi: 10.1016/j.drudis.2023.103675
  20. A. Baiardi, M. Christandl, and M. Reiher, "Quantum computing for molecular biology," ChemBioChem, vol. 24, no. 13, p. e202300120, Jun. 2023. doi: 10.48550/arXiv.2212.12220
  21. D. Deutsch, "Quantum theory, the Church–Turing principle and the universal quantum computer," Proceedings of the Royal Society A, vol. 400, no. 1818, pp. 97–117, Jul. 1985. doi: 10.1098/rspa.1985.0070
  22. A. Einstein, B. Podolsky, and N. Rosen, "Can quantum-mechanical description of physical reality be considered complete?," Physical Review, vol. 47, no. 10, p. 777, May 1935. doi: 10.1103/PhysRev.47.777
  23. F. Jazaeri, A. Beckers, A. Tajalli, and J. M. Sallese, “A review on quantum computing: From qubits to front-end electronics and cryogenic MOSFET physics.” in 2019 MIXDES- 26th International Conference "Mixed Design of Integrated Circuits and Systems, 27-29 June 2019, Rzeszow, Poland. Available: https://ieeexplore.ieee.org/document/8787164. [Accessed: 05 August. 2019].
  24. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th ed. Cambridge, UK: Cambridge University Press, 2010.
  25. F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends,. and J. M. Martinis, "Quantum supremacy using a programmable superconducting processor," Nature, Oct. 2019, doi:10.1038/s41586-019-1666-5
  26. H. S. Zhong, Y. H. Deng, J. Qin, H. Wang, M. C. Chen, L. C. Peng,. and J. W. Pan, "Phase-programmable gaussian boson sampling using stimulated squeezed light," Physical Review Letters, vol. 127, no. 18, p. 180502, Oct. 2021. doi: 10.1103/PhysRevLett.127.180502
  27. J. Preskill, "Quantum computing 40 years later," in Feynman Lectures on Computation, pp. 193–244, CRC Press, 2023.
  28. M. U. Khan et al., "Error Mitigation in the NISQ Era: Applying Measurement Error Mitigation Techniques to Enhance Quantum Circuit Performance," Mathematics 2024, vol. 12, no. 14, p. 2235, July 2024. doi: 10.3390/math12142235
  29. M. Anthony and C. Yu., Scaling up superconducting quantum computers, Nature Electronics, vol. 8, doi:10.1038/s41928-025-01381-7
  30. S. Bravyi, G. Carleo, D. Gosset, and Y. Liu, "A rapidly mixing Markov chain from any gapped quantum many-body system," Quantum, vol. 7, p. 1173, Nov. 2023. doi: 10.22331/q-2023-11-07-1173
  31. A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor,. and H. Weinfurter, "Elementary gates for quantum computation," Physical Review A, vol. 52, no. 5, pp. 3457–3467, March 1995. doi: 10.48550/arXiv.quant-ph/9503016
  32. M. Suchara, Y. Alexeev, F. Chong, H. Finkel, H. Hoffmann, J. Larson, J. Osborn, and G. Smith, “Hybrid quantum-classical computing architectures,” in Proceedings of the SC18 Workshops: Post-Moore Era Supercomputing, Nov. 11, 2018, Dallas, TX, USA [Workshop]. Los Alamitos, CA: IEEE, 2018. Available: IEEE Xplore, http://sc18.supercomputing.org/proceedings/workshops/workshop_files/ws_pmes110s1-file1.pdf. [Accessed: Aug. 26, 2025].
  33. D. Fan, G. Liu, S. Li, M. Gong, D. Wu, Y. Zhang, C. Zha, F. Chen, S. Cao, Y. Ye, et al., “Calibrating quantum gates up to 52 qubits in a superconducting processor,” npj Quantum Information 11, 33, Feb. 2025. doi: 10.1038/s41534-025-00983-5
  34. Y. Aharonov, D. Z. Albert, and L. Vaidman, "How the result of a measurement of a component of the spin of a spin-1/2 particle can turn out to be 100," Physical Review Letters, vol. 60, pp. 1351–1354, April 1988. doi: 10.1103/PhysRevLett.60.1351
  35. K. Jacobs, Quantum Measurement Theory and its Applications, 1st ed. Cambridge: Cambridge University Press, 2014.
  36. W. H. Zurek, "Decoherence, Einselection, and the Quantum Origins of the Classical," Reviews of Modern Physics, vol. 75, pp. 715–775, June 2003. doi: 10.1103/RevModPhys.75.715 [37] Y. Kitaev, "Fault-Tolerant Quantum Computation by Anyons," Annals of Physics, vol. 303, no. 1, pp. 2–30, Jan. 2003. doi: 10.1016/S0003-4916(02)00018-0
  37. S. Haroche, "Nobel Lecture: Controlling Photons in a Box and Exploring the Quantum-to-Classical Boundary," Reviews of Modern Physics, vol. 85, pp. 1083–1102, July 2013. doi: 10.1103/RevModPhys.85.1083
  38. [39 F. Marxer et al., “Above 99.9 % fidelity single-qubit gates, two-qubit gates, and readout in a single superconducting quantum device,” Quantum Physics, vol. 2508.16437, Aug. 2025. doi:10.48550/arXiv.2508.16437
  39. J. Preskill, "Quantum computing in the NISQ era and beyond," Quantum, vol. 2, p. 79, July 2018. doi: 10.48550/arXiv.1801.00862
  40. S. Ebadi et al., " Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator," Nature, vol. 595, no. 7865, pp. 227–232, Jul. 2021, doi: 10.48550/arXiv.2012.12281
  41. J. Alicea, "New Directions in the Pursuit of Majorana Fermions in Solid State Systems," Reports on Progress in Physics, vol. 75, no. 7, p. 076501, February 2012. doi: 10.1088/0034-4885/75/7/076501
  42. P. W. Shor, "Algorithms for quantum computation: discrete logarithms and factoring," in Proceedings 35th Annual Symposium on Foundations of Computer Science, Nov. 20–22 1994, Santa Fe, NM, USA. Los Alamitos. Available: https://ieeexplore.ieee.org/document/365700 [Accessed: 06 August 2002].
  43. L. K. Grover, " A fast quantum mechanical algorithm for database search," IEEE Trans. Quantum Eng., vol. 3, p. 3103523, Nov. 1996, doi: 10.48550/arXiv.quant-ph/960504
  44. J. Park, S. Samarakoon, H. Shiri, M. K. Abdel-Aziz, T. Nishio, A. Elgabli, and M. Bennis, "Extreme ultra-reliable and low-latency communication," Nature Electronics, vol. 5, no. 3, pp. 133–141, Mar. 2022. doi: 10.1038/s41928-022-00728-8
  45. F. Zaman, A. Farooq, M. A. Ullah, H. Jung, H. Shin, and M. Z. Win, "Quantum machine intelligence for 6G URLLC," IEEE Wireless Communications, vol. 30, no. 2, pp. 22–30, Apr. 2023. doi: 10.1109/MWC.003.2200382
  46. H. Urgelles, S. Maheshwari, S. S. Nande, R. Bassoli, F. H. Fitzek, and J. F. Monserrat, "In-network quantum computing for future 6G networks," Advanced Quantum Technologies, vol. 8, no. 2, p. 2300334, Mar. 2024. doi: 10.1002/qute.202300334
  47. F. Phillipson, "Quantum computing in telecommunication—a survey," Mathematics, vol. 11, no. 15, p. 3423, Aug. 2023. doi: 10.3390/math11153423
  48. V. Rishiwal, U. Agarwal, M. Yadav, S. Tanwar, D. Garg, and M. Guizani, "A new alliance of machine learning and quantum computing: Concepts, attacks, and challenges in IoT networks," IEEE Internet of Things Journal, vol. 12, no. 12, pp. 18865–18886, Jun. 2025. doi: 10.1109/JIOT.2025.3535414
  49. D. Javeed, M. S. Saeed, I. Ahmad, M. Adil, P. Kumar, and A. N. Islam, "Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions," Future Generation Computer Systems, vol. 160, pp. 577–597, Nov. 2024, doi: 10.1016/j.future.2024.06.023
  50. G. Barillaro, A. Boella, F. Gandino, M. G. Vakili, E. Giusto, G. Mondo, B. Montrucchio, A. Scarabosio, A. Scionti, O. Terzo, and G. Vitali, "Comparison of heuristic approaches to PCI planning for quantum computers," in Proc. 2023 IEEE Int. Conf. Consumer Electronics (ICCE), Las Vegas, NV, USA, Jan. 6–8, 2023, pp. 1–6. doi: 10.1109/ICCE56470.2023.10043394
  51. M. Kim, S. Kasi, P. A. Lott, D. Venturelli, J. Kaewell, and K. Jamieson, "Heuristic quantum optimization for 6G wireless communications," IEEE Network, vol. 35, no. 4, pp. 8–15, Aug. 2021. doi: 10.1109/MNET.012.2000770
  52. C. Wang and E. Jonckheere, "Simulated versus reduced noise quantum annealing in maximum independent set solution to wireless network scheduling," Quantum Information Processing, vol. 18, no. 1, Nov. 2018. doi: 10.1007/s11128-018-2117-1
  53. J. Choi, S. Oh, and J. Kim, "Quantum approximation for wireless scheduling," Applied Sciences, vol. 10, no. 20, p. 7116, Oct. 2020. doi: 10.3390/app10207116 [55] M. Saravanan and R. P. Sircar, "Quantum evolutionary algorithm for scheduling resources in virtualized 5G RAN environment," in Proc. IEEE 4th 5G World Forum (5GWF), Montreal, QC, Canada, Oct. 13–15, 2021, pp. 111–116. doi: 10.1109/5GWF52925.2021.00027
  54. H. Urgelles, P. Picazo-Martinez, D. Garcia-Roger, and J. F. Monserrat, "Multi-objective routing optimization for 6G communication networks using a quantum approximate optimization algorithm," Sensors, vol. 22, no. 19, p. 7570, Oct. 2022. doi: 10.3390/s22197570
  55. J. Wurtz, P. L. Lopes, C. Gorgulla, N. Gemelke, A. Keesling, and S. Wang, "Industry applications of neutral-atom quantum computing solving independent set problems," arXiv preprint arXiv:2205.08500, 2022. doi: 10.48550/arXiv.2205.08500
  56. M. Zohaib, F. S. Altuwaijri, and S. Hyrynsalmi, "Integrating quantum computing and blockchain: Building the foundations of secure, efficient 6G technology," in Proc. 1st ACM Int. Workshop Quantum Software Engineering: The Next Evolution, Porto de Galinhas, Brazil, Jul. 16, 2024, pp. 27–34. doi: 10.1145/3663531.3664755
  57. M. Cerezo, G. Verdon, H. Y. Huang, L. Cincio, and P. J. Coles, "Challenges and opportunities in quantum machine learning," Nature Computational Science, vol. 2, no. 9, pp. 567–576, Sep. 2022. doi: 10.1038/s43588-022-00311-3
  58. A. Jenber Belay, Y. M. Walle, and M. B. Haile, "Deep ensemble learning and quantum machine learning approach for Alzheimer's disease detection," Scientific Reports, vol. 14, no. 1, p. 14196, Jun. 2024. doi: 10.1038/s41598-024-61452-1
  59. M. C. Caro, H. Y. Huang, M. Cerezo, K. Sharma, A. Sornborger, L. Cincio, and P. J. Coles, "Generalization in quantum machine learning from few training data," Nature Communications, vol. 13, no. 1, p. 4919, Aug. 2022. doi: 10.1038/s41467-022-32550-3
  60. B. Zhang, P. Xu, X. Chen, and Q. Zhuang, "Generative quantum machine learning via denoising diffusion probabilistic models," Physical Review Letters, vol. 132, no. 10, p. 100602, Mar. 2024. doi: 10.1103/PhysRevLett.132.100602
  61. J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, "Barren plateaus in quantum neural network training landscapes," Nature Communications, vol. 9, no. 1, p. 4812, Nov. 2018. doi: 10.1038/s41467-018-07090-4
  62. M. Larocca, F. Sauvage, F. M. Sbahi, G. Verdon, P. J. Coles, and M. Cerezo, "Group-invariant quantum machine learning," PRX Quantum, vol. 3, no. 3, p. 030341, Sep. 2022. doi: 10.1103/PRXQuantum.3.030341
  63. R. Sweke, E. Recio-Armengol, S. Jerbi, E. Gil-Fuster, B. Fuller, J. Eisert, and J. J. Meyer, "Potential and limitations of random Fourier features for dequantizing quantum machine learning," Quantum, vol. 9, p. 1640, Feb. 2025. doi: 10.22331/q-2025-02-20-1640
  64. H. Y. Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, and J. R. McClean, "Power of data in quantum machine learning," Nature Communications, vol. 12, no. 1, p. 2631, May 2021. doi: 10.1038/s41467-021-22539-9
  65. M. T. West, J. Heredge, M. Sevior, and M. Usman, "Provably trainable rotationally equivariant quantum machine learning," PRX Quantum, vol. 5, no. 3, p. 030320, Jul. 2024. doi: 10.1103/PRXQuantum.5.030320
  66. S. Jerbi, L. J. Fiderer, H. Poulsen Nautrup, J. M. Kübler, H. J. Briegel, and V. Dunjko, "Quantum machine learning beyond kernel methods," Nature Communications, vol. 14, no. 1, p. 517, Jan. 2023. doi:10.1038/s41467-023-36159-y
  67. A. Senokosov, A. Sedykh, A. Sagingalieva, B. Kyriacou, and A. Melnikov, "Quantum machine learning for image classification," Machine Learning: Science and Technology, vol. 5, no. 1, p. 015040, Mar. 2024. doi: 10.1088/2632-2153/ad2aef
  68. S. Jerbi, C. Gyurik, S. C. Marshall, R. Molteni, and V. Dunjko, "Shadows of quantum machine learning," Nature Communications, vol. 15, no. 1, p. 5676, Jul. 2024. doi: 10.1038/s41467-024-49877-8
  69. Y. Liu, E. J. Kuo, C. H. A. Lin, S. Chen, J. G. Young, Y. J. Chang, and M. H. Hsieh, "Training classical neural networks by quantum machine learning," in Proc. 2024 IEEE Int. Conf. Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 34–38. doi: 10.1109/QCE60285.2024.10248
  70. M. Akrom, "Quantum support vector machine for classification task: A review," Journal of Multiscale Materials Informatics, vol. 1, no. 2, pp. 1–8, Aug. 2024. doi: 10.62411/jimat.v1i2.10965
  71. L. Li, X. Zhang, Z. Cui, W. Xu, X. Xu, J. Wang, and R. Shu, "An overview of quantum machine learning research in China," Applied Sciences, vol. 15, no. 5, p. 2555, Mar. 2025. doi: 10.3390/app15052555
  72. T. M. Khan and A. Robles-Kelly, "Machine learning: Quantum vs classical," IEEE Access, vol. 8, pp. 219275–219294, Dec. 2020. doi: 10.1109/ACCESS.2020.3041719
  73. P. O. Dral, "Quantum chemistry in the age of machine learning," The Journal of Physical Chemistry Letters, vol. 11, no. 6, pp. 2336–2347, Mar. 2020. doi: 10.1021/acs.jpclett.9b03664
  74. M. Y. Melnikov, A. A. Shashkin, V. T. Dolgopolov, A. Y. Zhu, S. V. Kravchenko, S. H. Huang, and C. W. Liu, "Quantum phase transition in ultrahigh mobility SiGe/Si/SiGe two-dimensional electron system," Physical Review B, vol. 99, no. 8, p. 081106, Feb. 2019. doi: 10.1103/PhysRevB.99.081106
  75. A. Vijay, H. Bhargava, A. Pareek, P. Suravajhala, and A. Sharma, "Quantum machine learning for biological applications," in Bioinformatics and Computational Biology. Boca Raton, FL, USA: Chapman and Hall/CRC, 2023, pp. 75–86. doi: 10.1201/9781003331247-8
  76. A. Cordier, N. P. Sawaya, G. G. Guerreschi, and S. K. McWeeney, "Biology and medicine in the landscape of quantum advantages," Journal of the Royal Society Interface, vol. 19, no. 196, p. 20220541, Nov. 2022. doi: 10.1098/rsif.2022.0541
  77. D. Maheshwari, U. Ullah, P. A. O. Marulanda, A. G. O. Jurado, I. D. Gonzalez, J. M. O. Merodio, and B. Garcia-Zapirain, "Quantum machine learning applied to electronic healthcare records for ischemic heart disease classification," Human-Centric Computing and Information Sciences, vol. 13, no. 6, pp. 1–18, Feb. 2023. doi: 10.22967/HCIS.2023.13.006
  78. Q. Bai and X. Hu, "Superposition-enhanced quantum neural network for multi-class image classification," Chinese Journal of Physics, vol. 89, pp. 378–389, Jun. 2024. doi: 10.1016/j.cjph.2024.03.026
  79. Z. Li, T. Xiao, X. Deng, G. Zeng, and W. Li, "Optimizing variational quantum neural networks based on collective intelligence," Mathematics, vol. 12, no. 11, p. 1627, May 2024. doi: 10.3390/math12111627
  80. C. Gong, W. Guan, H. Zhu, A. Gani, and H. Qi, "Network intrusion detection based on variational quantum convolution neural network," The Journal of Supercomputing, vol. 80, no. 9, pp. 12743–12770, Jun. 2024. doi: 10.1007/s11227-024-05919-y
  81. Y. Chen and W. Fang, "Multi-scale feature fusion quantum depthwise convolutional neural networks for text classification," arXiv preprint arXiv:2405.13515, 2024. doi: 10.48550/arXiv.2405.13515
  82. N. Sachdeva et al., "Quantum optimization using a 127-qubit gate-model IBM quantum computer can outperform quantum annealers for nontrivial binary optimization problems," arXiv preprint arXiv:2405.13515, 2024. doi: 10.48550/arXiv.2406.01743 [85] R. Zhang, J. Wang, N. Jiang, and Z. Wang, "Quantum support vector machine without iteration," Information Sciences, vol. 635, pp. 25–41, Jul. 2023. doi: 10.1016/j.ins.2023.03.106
  83. A. Tedyyana, A. A. Ahmad, M. R. Idrus, M. Shabli, A. Hanis, M. A. Abu Seman, and A. H. Abd Razak, "Enhance telecommunication security through the integration of support vector machines," International Journal of Advanced Computer Science & Applications, vol. 15, no. 3, p. 633, 2024. doi: 10.14569/ijacsa.2024.0150364
  84. M. Kalinin and V. Krundyshev, "Security intrusion detection using quantum machine learning techniques," Journal of Computer Virology and Hacking Techniques, vol. 19, no. 1, pp. 125–136, Mar. 2023. doi: 10.1007/s11416-022-00435-0
  85. N. Innan, B. K. Behera, S. Al-Kuwari, and A. Farouk, "QNN-VRCS: A quantum neural network for vehicle road cooperation systems," IEEE Transactions on Intelligent Transportation Systems, Jan. 2025. doi: 10.1109/TITS.2025.3538786
  86. Y. Peng, X. Li, Z. Liang, and Y. Wang, "HyQ2: A Hybrid Quantum Neural Network for NextG Vulnerability Detection," IEEE Transactions on Quantum Engineering, vol. 5, pp.1-19,2024. doi: 10.1109/TQE.2024.3481280
  87. B. Narottama and S. Y. Shin, "Federated quantum neural network with quantum teleportation for resource optimization in future wireless communication," IEEE Transactions on Vehicular Technology, vol. 72, no. 11, pp. 14717-14733, 2023. doi: 10.1109/TVT.2023.3280459
  88. P. Punnen, The quadratic unconstrained binary optimization problem: Theory, Algorithms, and Applications,, vol. 10, Springer International Publishing, 2022, pp. 978-3.
  89. M. O. Butt, N. Waheed, T. Q. Duong, and W. Ejaz, "Quantum-Inspired Resource Optimization for 6G Networks: A Survey," IEEE Communications Surveys & Tutorials, 2024. doi: 10.1109/COMST.2024.3519865
  90. T. Albash and D. A. Lidar, "Adiabatic quantum computation," Reviews of Modern Physics, vol. 90, no. 1, pp. 15002-15066, Jan 2018. doi: 10.1103/RevModPhys.90.015002
  91. F. Vista, "Quantum-Aided modeling and design techniques for advanced wireless network architectures," Ph.D. dissertation, Politecnico di Bari, Bari, Italy, 2024.
  92. E. Farhi, J. Goldstone, and S. Gutmann, "A quantum approximate optimization algorithm," arXiv preprint arXiv:1411.4028, 2014. doi: 10.48550/arXiv.1411.4028
  93. O. Bouchmal, B. Cimoli, R. Stabile, J. V. Olmos, and I. T. Monroy, "Quantum Approximate Optimization Algorithm for Routing Optimization in 6G Optical Networks,” in 2024 International Conference on Optical Network Design and Modeling, ONDM, Madrid, Spain, May 2024, IEEE, 2024. doi: 10.23919/ONDM61578.2024.10582687
  94. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, et al., "The variational quantum eigensolver: a review of methods and best practices," Physics Reports, vol. 986, pp. 1-128, 2022. doi: 10.1016/j.physrep.2022.08.003
  95. Z. Yan, H. Zhou, J. Pei, A. Kaushik, H. Tabassum, and P. Wang, "CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks," arXiv preprint arXiv:2501.08418, 2025. doi: 10.48550/arXiv.2501.08418
  96. A. Das and B. K. Chakrabarti, Eds., Quantum annealing and related optimization methods, vol. 679, Springer Science & Business Media, 2005.
  97. T. Q. Dinh, S. H. Dau, E. Lagunas, S. Chatzinotas, D. N. Nguyen, and D. T. Hoang, "Quantum Annealing for Complex Optimization in Satellite Communication Systems," IEEE Internet of Things Journal, vol. 12, no. 4, pp.3771-3784, 2024. doi: 10.1109/JIOT.2024.3481373
  98. J. Son, M. Gluza, R. Takagi, and N. H. Ng, "Quantum dynamic programming," Physical Review Letters, vol. 134, pp. 180602-180610, May 2025. doi: 10.1103/PhysRevLett.134.180602
  99. D. Alanis, P. Botsinis, Z. Babar, H. V. Nguyen, D. Chandra, S. X. Ng, and L. Hanzo, "A quantum-search-aided dynamic programming framework for pareto optimal routing in wireless multihop networks," IEEE Transactions on Communications, vol. 66, no. 8, pp. 3485-3500, 2018. doi: 10.1109/TCOMM.2018.2803068
  100. P. Ronagh, "Quantum algorithms for solving dynamic programming problems," arXiv preprint arXiv:1906.02229, vol. 187, 2019.
  101. C. Li, Z. Liu, Y. Song, H. Liu, H. Liu, and X. Liu, "Quantum Computing Approaches to Optimize Employee Scheduling in Multi-task Call Centers," in The International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE), Singapore, 2023, pp. 3-9, Springer Nature Singapore, 2024. doi: 10.1007/978-981-97-0194-0_1
  102. S. Caleb and S. J. J. Thangaraj, "Quantum-assisted spectrum sharing in cognitive self-organizing networks," in 2023 4th International Conference on Smart Electronics and Communication, ICOSEC, 2023, pp. 579-584, IEEE, 2023. doi: 10.1109/ICOSEC58147.2023.10275948
  103. P. Whig, R. Remala, K. R. Mudunuru, and S. J. Quraishi, "Integrating AI and quantum technologies for sustainable supply chain management," Quantum Computing and Supply Chain Management: A New Era of Optimization, pp. 267-283, 2024. doi: 10.4018/979-8-3693-4107-0.ch018
  104. M. Ramasamy, B. Jegan, M. Naveenganesh, and S. Raghavendar, "Enhancing Automobile Motion Planning Algorithms using Quantum Computing: A Systematic Review," in 2024 10th International Conference on Communication and Signal Processing, ICCSP, April 2024, India, IEEE, 2024. doi: 10.1109/ICCSP60870.2024.10543893
  105. M. S. Moreolo, M. Iqbal, A. Villegas, L. Nadal, R. Casellas, and R. Muñoz, "Continuous-Variable Quantum Key Distribution for Enabling Sustainable Secure 6G Networks," in 2024 International Conference on Optical Network Design and Modeling, ONDM, Madrid, Spain, May 2024, IEEE, 2024. doi: 10.23919/ONDM61578.2024.10582608
  106. Y. Zhang, Y. Bian, Z. Li, S. Yu, and H. Guo, "Continuous-variable quantum key distribution system: Past, present, and future," Applied Physics Reviews, vol. 11, no. 1, 2024. doi: 10.1063/5.0179566
  107. R. Bavdekar, E. J. Chopde, A. Agrawal, A. Bhatia, and K. Tiwari, "Post quantum cryptography: a review of techniques, challenges and standardizations," in 2023 International Conference on Information Networking, ICOIN, 2023, Bangkok, Kingdom of Thailand, IEEE. doi: 10.1109/ICOIN56518.2023.10048976
  108. H. Nejatollahi, N. Dutt, S. Ray, F. Regazzoni, I. Banerjee, and R. Cammarota, "Post-quantum lattice-based cryptography implementations: A survey," ACM Computing Surveys, vol. 51, no. 6, pp. 1-41, 2019. doi: 10.1145/3292548
  109. A. Khalid, S. McCarthy, M. O’Neill, and W. Liu, "Lattice-based cryptography for IoT in a quantum world: Are we ready?," in 8th International Workshop on Advances in Sensors and Interfaces, IWASI, June 13-14, 2019, Otranto, Italy, IEEE, 2018. doi: 10.1109/IWASI.2019.8791343
  110. X. Wang, G. Xu, and Y. Yu, "Lattice-based cryptography: A survey," Chinese Annals of Mathematics, Series B, vol. 44, no. 6, pp. 945-960, 2023. doi: 10.1007/s11401-023-0053-6
  111. R. Kuang, M. Perepechaenko, and M. Barbeau, "A new post-quantum multivariate polynomial public key encapsulation algorithm," Quantum Information Processing, vol. 21, no. 10, p. 360, 2022. doi: 10.1007/s11128-022-03712-5
  112. G. Mahajan, S. Tiwari, R. Sharma, and R. K. Gupta, "Discrete mathematics for strengthening multivariate polynomial cryptography by addressing vulnerabilities." Journal of Discrete Mathematical Sciences & Cryptography, vol. 27, no. 7, pp. 2123-2132, 2024. doi: 10.47974/JDMSC-2085
  113. E. Dritsas and M. Trigka, "A Survey on Cybersecurity in IoT," Future Internet, vol. 17, no. 1, 2025. doi: 10.3390/fi17010030
  114. B. B. Gupta, D. Kalra, and A. Almomani, Innovations in Modern Cryptography, IGI Global, 2024. doi: 10.4018/979-8-3693-5330-1
  115. E. Fathalla and M. Azab, "Beyond Classical Cryptography: A Systematic Review of Post-Quantum Hash-Based Signature Schemes, Security, and Optimizations," IEEE Access, vol. 12, pp. 175969-175987, 2024. doi: 10.1109/ACCESS.2024.3485602
  116. Q. Berthet, A. Upegui, L. Gantel, A. Duc, and G. Traverso, "An area-efficient SPHINCS+ post-quantum signature coprocessor," in 2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW, Virtually, pp. 180-187, IEEE, 2021. doi: 10.1109/IPDPSW52791.2021.00034 [120] P. Kampanakis, P. Panburana, M. Curcio, C. Shroff, and M. M. Alam, "Post-quantum LMS and SPHINCS+ hash-based signatures for UEFI secure boot," Cryptology ePrint Archive, 2021. Available: Cryptology ePrint Archive, https://eprint.iacr.org/2021/041 [Accessed: 10 Jan. 2025]
  117. R. Zhou, H. Guo, F. E. Teo, and S. Bakiras, "A Survey on Post-Quantum Cryptography for 5G/6G Communications," in 2023 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI, Singapure, Dec. 11-13, IEEE, 2023. doi: 10.1109/SOLI60636.2023.10425346
  118. C. Balamurugan, K. Singh, G. Ganesan, and M. Rajarajan, "Post-quantum and code-based cryptography—some prospective research directions," Cryptography, vol. 5, no. 4, pp. 38, 2021. doi: 10.3390/cryptography5040038
  119. R. Overbeck and N. Sendrier, Code-based cryptography, in Post-quantum cryptography, Springer, Berlin, Heidelberg, 2009, pp. 95-145. doi: 0.1007/978-3-540-88702-7_4
  120. M. R. Maganti, D. Mangamma, V. K. Suri, S. V. Swamy, A. Mohammad, and V. Shariff, "Securing Asymmetric Key Cryptography in 6G Wireless and Mobile Environments: A Comprehensive Review and Proposed Hybrid Cryptosystem," in 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC, 27-28 July, India, IEEE, 2024. doi: 10.1109/AIC61668.2024.10730910
  121. R. Azarderakhsh, D. Jao, K. Kalach, B. Koziel, and C. Leonardi, "Key compression for isogeny-based cryptosystems," in Proceedings of the 3rd ACM International Workshop on ASIA Public-Key Cryptography, Association for Computing Machinery, May 2016, New York, NY, USA, pp. 1-10, 2016. doi: 10.1145/2898420.289842
  122. M. Naehrig and J. Renes, "Dual isogenies and their application to public-key compression for isogeny-based cryptography," in International Conference on the Theory and Application of Cryptology and Information Security, Cham: Springer, 2019. doi: /10.1007/978-3-030-34621-8_9
  123. Y. F. Lai, Advanced Isogeny-based Cryptosystems, doctoral dissertation, University of Auckland, 2023.
  124. V. L. S. S. Varnita, K. Subramanyam, T. P. Narasimha, H. Bhandari, and A. SA, "A study on isogeny based cryptography," in 2024 International Conference on Electronics, Computing, Communication and Control Technology, ICECCC, 2-3 May, India, IEEE, 2024. doi: 10.1109/ICECCC61767.2024.10593878
  125. M. Chase, D. Derler, S. Goldfeder, C. Orlandi, S. Ramacher, C. Rechberger, et al., "Post-quantum zero-knowledge and signatures from symmetric-key primitives," Association for Computing Machinery, pp. 1825-1842, 2017. doi: 10.1145/3133956.3133997
  126. J. Partala, "Post-quantum cryptography in 6G," 6G Mobile Wireless Networks, pp. 431-448, 2021. doi: 10.1007/978-3-030-72777-2_20
  127. Y. Qian, X. Wang, Y. Du, X. Wu, and D. Tao, "The dilemma of quantum neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5603-5615, 2022. doi: 10.1109/TNNLS.2022.3208313
  128. M. Belkhir, H. Benkaouha, and E. Benkhelifa, "Quantum vs Classical Computing: A Comparative Analysis," in Proc. 7th Int. Conf. on Fog and Mobile Edge Computing (FMEC), 12-15 December 2022, Paris,France. Available: https://ieeexplore.ieee.org/document/10062753. [Accessed: 14 March 2023].
  129. J. Li, F. Gao, S. Lin, M. Guo, Y. Li, H. Liu,. and Q. Wen, "Quantum k-fold cross-validation for nearest neighbor classification algorithm," Physica A: Statistical Mechanics and its Applications, vol. 611, p. 128435, February 2023. doi: 10.1016/j.physa.2022.128435
  130. S. Guo, J. Sun, H. Qian, M. Gong, Y. Zhang, F. Chen,. and J. W. Pan, "Experimental quantum computational chemistry with optimized unitary coupled cluster ansatz," Nature Physics, vol. 20, no. 8, pp. 1240–1246, August 2024. doi: 10.1038/s41567-024-02530-z
  131. R. Izsák, C. Riplinger, N. S. Blunt, B. de Souza, N. Holzmann, O. Crawford,. and P. Schopf, "Quantum computing in pharma: A multilayer embedding approach for near future applications," Journal of Computational Chemistry, vol. 44, no. 3, pp. 406–421, Jun 2022. doi: 10.48550/arXiv.2202.04460
  132. F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends,. and J. M. Martinis, "Quantum supremacy using a programmable superconducting processor," Nature, Oct. 2019, doi:10.1038/s41586-019-1666-5
  133. H. S. Zhong, Y. H. Deng, J. Qin, H. Wang, M. C. Chen, L. C. Peng,. and J. W. Pan, "Phase-programmable gaussian boson sampling using stimulated squeezed light," Physical Review Letters, vol. 127, no. 18, p. 180502, Oct. 2021. doi: 10.1103/PhysRevLett.127.180502
  134. M. Anthony and C. Yu., Scaling up superconducting quantum computers, Nature Electronics, vol. 8, doi:10.1038/s41928-025-01381-7
  135. A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor,. and H. Weinfurter, "Elementary gates for quantum computation," Physical Review A, vol. 52, no. 5, pp. 3457–3467, March 1995. doi: 10.48550/arXiv.quant-ph/9503016
  136. D. Fan, G. Liu, S. Li, M. Gong, D. Wu, Y. Zhang, C. Zha, F. Chen, S. Cao, Y. Ye, et al., “Calibrating quantum gates up to 52 qubits in a superconducting processor,” npj Quantum Information 11, 33, Feb. 2025. doi: 10.1038/s41534-025-00983-5
  137. P. W. Shor, "Algorithms for quantum computation: discrete logarithms and factoring," in Proceedings 35th Annual Symposium on Foundations of Computer Science, Nov. 20–22 1994, Santa Fe, NM, USA. Los Alamitos. Available: https://ieeexplore.ieee.org/document/365700 [Accessed: 06 August 2002].
  138. P. Punnen, The quadratic unconstrained binary optimization problem: Theory, Algorithms, and Applications,, vol. 10, Springer International Publishing, 2022, pp. 978-3.
  139. V. L. S. S. Varnita, K. Subramanyam, T. P. Narasimha, H. Bhandari, and A. SA, "A study on isogeny based cryptography," in 2024 International Conference on Electronics, Computing, Communication and Control Technology, ICECCC, 2-3 May, India, IEEE, 2024. doi: 10.1109/ICECCC61767.2024.10593878