Daemons-Q/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

metric	value
aggregate_accuracy	0.986667
classical_rf_macro_f1	0.986629
n_classes	8
n_samples	2400
quantum_qnn_accuracy	0.875
quantum_qnn_binary_f1	0.933333
quantum_qnn_macro_f1	0.466667
quantum_qnn_n_params	25
quantum_svc_accuracy	0.908333
quantum_svc_binary_f1	0.945274
quantum_svc_macro_f1	0.831611
wall_clock_s	267.1

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 What remains well-founded is caution about evaluation quality rather than a universal ranking: arXiv:2512.05069 was withdrawn by its own authors for evaluation that was 'insufficient to fully support the conclusions'.
• 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.