---
license: other
license_name: generated-by-this-repository
library_name: transformers
pipeline_tag: tabular-classification
tags:
  - quantum-safe
  - privacy-preserving
  - telecom-ids
---

# Quantum-kernel SVC + variational QNN

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

Part of [QSMPC-QKD-QHE-AI-Hybrid](https://github.com/thedaemon-wizard/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 What remains well-founded is caution about evaluation quality rather than a universal ranking: arXiv:2512.05069 was withdrawn by its own authors for evaluation that was 'insufficient to fully support the conclusions'.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.