# VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet

arXiv: https://arxiv.org/abs/2608.18572 · HTML: https://mlsanity.com/paper/2608.18572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)
- [CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting](https://mlsanity.com/paper/2409.18874/) (2024)
- [A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions](https://mlsanity.com/paper/2009.12164/) (2020)

## Abstract (arXiv)

Tactile Internet services couple cyber events directly to physical actuation, so security decisions must improve risk discrimination without perturbing the control path. This paper presents VQC-ZTI, a split-plane Variational Quantum Classifier framework for zero-trust protection of Tactile Internet services, in which an off-path VQC analyzes encrypted-flow telemetry while an on-path policy engine applies cached deterministic grant, restrict, step-up, and deny actions. By decoupling anomaly scoring from enforcement, VQC-ZTI preserves predictable control behavior and allows detector sensitivity and policy aggressiveness to be tuned independently. We evaluate the framework on CESNET-derived aggregated traffic using random, entity-group, and temporal holdouts with a hybrid PyTorch-PennyLane implementation. The full-hybrid Quantum Neural Network achieves mean areas under the receiver operating characteristic curve of 0.9981, 0.9974, and 0.9941 and reduces the false-positive rate relative to ExtraTrees by 44.6%, 49.6%, and 67.9%, respectively. A representative component-timing decomposition further illustrates that batched VQC scoring remains in the asynchronous evidence path rather than the immediate enforcement path.