qsh-telecom-qml-ids

Quantum-kernel SVC + variational QNN

PCA -> angle embedding -> StronglyEntanglingLayers; fidelity quantum kernel

Part of QSMPC-QKD-QHE-AI-Hybrid, a quantum-safe orchestration demo. This is the plaintext model for the telecom_ids use case; the encrypted path runs a distilled student, not this model.

Measured performance

Published baselines this is measured against

• Target metric: macro F1 (synthetic QKD-attack telemetry)
• Baseline: Architecture showcase, not a performance claim. On THIS project's synthetic telemetry neither quantum model beats the classical random forest (RF macro F1 0.987 vs variational QNN 0.467). Published results are MIXED, not uniformly below a random forest -- an earlier revision of this note claimed they were, and the very paper cited here contradicts it: Al-Kuwari et al. (arXiv:2509.14282) Table VIII reports a hybrid QLSTM at 94.7% accuracy / 94.7% F1 against their own random-forest baseline on the same generated data. What remains well-founded is caution about evaluation quality rather than a universal ranking: arXiv:2512.05069 was withdrawn by its own authors for evaluation that was 'insufficient to fully support the conclusions'. That single withdrawal is thin support for a field-wide claim, so the anchor is now a survey: Kaissar, Bou Nassif & Bouridane, Future Internet 18(5):234 (2026-04-27, CC BY 4.0, DOI 10.3390/fi18050234), systematic to January 2026, finds that 'most current approaches rely on simulated quantum environments and legacy datasets, while evaluation practices remain inconsistent across studies' - active but methodologically early, which is a different and better-founded statement than 'worse than a random forest'. The Al-Kuwari paper is now peer-reviewed: IET Quantum Communication 7(1), DOI 10.1049/qtc2.70028 (2026-02-23). And the strongest 2026 result on this exact problem is CLASSICAL, which corroborates the framing here: IACR ePrint 2026/1282 reaches 96.9% recall / 97.6% precision with physics-aware temporal features plus XGBoost, against 17.3% recall for static QBER thresholding.
• Companion metric shown alongside: classical_rf_macro_f1 - reported together because the aggregate figure can look healthy while the class that matters is not.

Training data

• Dataset: QKD attack telemetry (synthetic, methodology-grounded)
• Licence: generated by this repository
• Source: https://arxiv.org/abs/2509.14282 (licence read 2026-08-03)

8 classes = 7 attack types + normal, per Al-Kuwari et al. (IET Quantum Communication 7(1), e70028, 2026). No commercial-OK real QKD-attack capture exists; this is labelled synthetic everywhere it is reported.

Notes and limitations

Shown as an architecture comparison against a random forest on identical data.

Honest scope

This model is published as part of a research proof of concept, not as a production system. Numbers above are what this repository measured on the split described, with the code in scripts/train/. Where a figure is carried from the literature rather than measured here, it is labelled as such.