Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two complementary experiments. First, we evaluate near-term quantum-kernel support vector machines (SVMs) on practical network-security classification tasks and compare them with classical SVM baselines on KDD Cup 1999, CICIDS2017, and BoT-IoT. Across these runs, quantum kernels are competitive. They can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in ...
| Authors | Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang et al. |
|---|---|
| Primary category | quant-ph |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.