Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks

Muhammad Afaq

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DOI: 10.1109/ojcoms.2025.3639435
Published: 2025
Container: IEEE Open Journal of the Communications Society
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1109/ojcoms.2025.3639435,
  title = {Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks},
  author = {Muhammad Afaq},
  year = {2025},
  journal = {IEEE Open Journal of the Communications Society},
  doi = {10.1109/ojcoms.2025.3639435},
  url = {https://doi.org/10.1109/ojcoms.2025.3639435}
}

RIS

TY  - JOUR
TI  - Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks
AU  - Muhammad Afaq
PY  - 2025
JO  - IEEE Open Journal of the Communications Society
DO  - 10.1109/ojcoms.2025.3639435
UR  - https://doi.org/10.1109/ojcoms.2025.3639435
ER  -

APA

Afaq, M. (2025). Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks. IEEE Open Journal of the Communications Society. https://doi.org/10.1109/ojcoms.2025.3639435

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crossref · retrieved 2026-09-26T15:37:23.960Z