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

Mubassir Serneabat Sudipto; Shakil Ahmed; Ashfaq Khokhar

cs.CRAug 19, 2026

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

Authors: Mubassir Serneabat Sudipto, Shakil Ahmed, Ashfaq Khokhar

Organizations: Iowa State University · Electrical and Computer Engineering, Iowa State University, Ames, Iowa, USA · Grand Valley State University · Computer Science, College of Computing, Grand Valley State University, Allendale, Michigan, USA · Kansas State University · Carl R. Ice College of Engineering, Kansas State University, Manhattan, Kansas, USA

Abstract

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.

Figures & tables

Fig. 1: Split-plane VQC–ZTI architecture with off-path evidence generation and on-path deterministic enforcement.

Panel of Figure 2 — Fig. 2: Representative full-hybrid QNN training behavior on the random split.

Split	Model	AUC	Accuracy	FPR
Group	QNN	$\mathbf{0.9974\pm 0.0009}$	$\mathbf{0.9762\pm 0.0028}$	$\mathbf{0.0241\pm 0.0042}$
	XT	$0.9932\pm 0.0014$	$0.9550\pm 0.0147$	$0.0479\pm 0.0191$
	RF	$0.9922\pm 0.0013$	$0.9531\pm 0.0147$	$0.0478\pm 0.0209$
	LR	$0.9525\pm 0.0075$	$0.8617\pm 0.0232$	$0.1520\pm 0.0307$
Random	QNN	$\mathbf{0.9981\pm 0.0004}$	$\mathbf{0.9756\pm 0.0042}$	$\mathbf{0.0270\pm 0.0050}$
	XT	$0.9928\pm 0.0006$	$0.9541\pm 0.0106$	$0.0487\pm 0.0137$

TABLE I: Cross-split detector performance.

Panel of Figure 4 — Fig. 3: Representative full-hybrid QNN and ExtraTrees results under (a) entity-group and (b) temporal distribution shift.

Split	Variant	AUC	Accuracy	FPR
Group	FH	$\mathbf{0.9974\pm 0.0009}$	$\mathbf{0.9762\pm 0.0028}$	$\mathbf{0.0241\pm 0.0042}$
	NH	$0.9956\pm 0.0006$	$0.9629\pm 0.0160$	$0.0395\pm 0.0218$
	SE	$0.9792\pm 0.0065$	$0.9409\pm 0.0088$	$0.0563\pm 0.0111$
	NE	$0.8693\pm 0.0194$	$0.8071\pm 0.0231$	$0.1922\pm 0.0245$
	PQC	$0.8656\pm 0.0198$	$0.8160\pm 0.0252$	$0.1755\pm 0.0411$
Random	FH	$\mathbf{0.9981\pm 0.0004}$	$\mathbf{0.9756\pm 0.0042}$	$\mathbf{0.0270\pm 0.0050}$

TABLE II: Hybrid QNN-model ablation across evaluation splits.

Path	Component	Baseline ZT	VQC–ZTI
Asynchronous	$T_{\mathrm{eval}}$ (Scoring)	0.85 ms	0.32 ms (Batched)
	$T_{\mathrm{PDP}}$ (Fusion)	0.25 ms	0.27 ms
	$T_{\mathrm{dist}}$ (Distribution)	0.40 ms	0.38 ms
On-path	$T_{\mathrm{PEP}}$ (Enforcement)	0.10 ms	0.10 ms

TABLE III: Split-plane component timings for scoring and enforcement.

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Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid quantum anomaly inference and introduce the Adaptive-Shot Variational Quantum Circuit (AS-VQC) policy. This validation-calibrated policy begins each record at 128 shots and cumulatively escalates through 256, 512, and 1024 shots only when the finite-shot anomaly score remains close to a validation-selected security threshold. The quantum scorer is evaluated as an off-path security analytics component rather than part of the haptic critical path. Using a 4,875-record CESNET-TimeSeries24-derived aggregate-flow benchmark, leakage-safe random, entity-group-disjoint, and temporal holdouts, and five trained quantum neural network (QNN) checkpoints per holdout, the primary AS-VQC-95 (beta = 0.95) policy averages 129.2, 276.9, and 131.2 shots per record, saving 87.4%, 73.0%, and 87.2% of the uniform 1024-shot baseline (Fixed-1024), respectively. The decision disagreement with analytic (exact-expectation) inference is 0.771%, 0.409%, and 0.635%, lower than both the uniform 128-shot baseline (Fixed-128) and a matched-budget shuffled-allocation control. Fixed-1024 remains more decision-stable, establishing a measurable reliability-resource trade-off rather than cost-free equivalence. A more conservative AS-VQC-99 (beta = 0.99) further reduces disagreement while using fewer than 512 average shots across all holdouts. These results show that finite quantum measurements can be treated as an inference resource and concentrated on boundary-sensitive TI-security decisions while exposing checkpoint-dependent escalation under unseen-entity conditions.

Abstract

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