Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints

Mubassir Serneabat Sudipto; Shakil Ahmed; Ashfaq Khokhar; Samir M. Iqbal

cs.CROct 5, 2026

Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints

Authors: Mubassir Serneabat Sudipto, Shakil Ahmed, Ashfaq Khokhar, Samir M. Iqbal

Organizations: College of Engineering Iowa State University Ames, Iowa, United States · College of Computing Grand Valley State University Allendale, Michigan, United States · Carl R. Ice College of Engineering Kansas State University Manhattan, Kansas, United States

Abstract

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.

Figures & tables

Figure 1: TI-oriented AS-VQC system model with off-path security analytics, validation-calibrated shot escalation, and on-path deterministic enforcement.

Holdout	AUC	AP	FPR	ECE	Disagree	Average Shots	Saving
Random	$0.9956\pm 0.0014$	$0.9760\pm 0.0050$	$0.0299\pm 0.0042$	$0.0257\pm 0.0062$	$0.0077\pm 0.0012$	$129.2\pm 0.4$	$87.39\pm 0.04$ %
Group	$0.9968\pm 0.0017$	$0.9835\pm 0.0092$	$0.0346\pm 0.0312$	$0.0174\pm 0.0038$	$0.0041\pm 0.0010$	$276.9\pm 327.3$	$72.96\pm 31.97$ %
Temporal	$0.9980\pm 0.0010$	$0.9895\pm 0.0060$	$0.0252\pm 0.0104$	$0.0211\pm 0.0076$	$0.0063\pm 0.0010$	$131.2\pm 2.0$	$87.19\pm 0.20$ %

Table 1: AS-VQC-95 test results (mean

\pm

sample SD across five checkpoints after within-checkpoint measurement averaging).

Figure 2: Finite-shot ranking and reliability. (a) ROC-AUC loss relative to analytic inference. (b) Decision disagreement versus average measurement budget; AS-VQC-95 is the primary adaptive policy.

Figure 3: TI-oriented security behavior. (a) AS-VQC-95 disagreement reduction versus matched-budget shuffling, with paired-bootstrap 95% intervals. (b) TPR/FPR operating points at the fixed validation-selected threshold.

Figure 4: AS-VQC-95 allocation behavior. (a) Fraction of records terminating at each cumulative shot budget. (b) Median analytic threshold distance by final allocation; error bars show pooled interquartile ranges.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Protocol	Seed	Train	Validation	Test
Random	42	1794	602	615
Random	43	1773	602	591
Random	44	1783	591	624
Random	45	1746	592	576
Random	46	1801	595	583
Group	42	1688	621	653

Appendix

Table 2: Post-filter train/validation/test sizes for the saved Paper-3 splits.

Figure 5: Calibration and checkpoint-level resource behavior. (a) Expected calibration error across fixed-shot and adaptive-shot measurement budgets. (b) AS-VQC-95 average measurement demand for each trained checkpoint, highlighting the high-escalation Group seed-46 run.

Holdout	Comparator	Mean $\Delta$ Disagree	95% CI
Random	Fixed-128	$-0.142$	$[-0.211,\ -0.074]$
Group	Fixed-128	$-0.396$	$[-0.840,\ -0.056]$
Temporal	Fixed-128	$-0.380$	$[-0.565,\ -0.129]$
Random	Budget-Shuffled-AS95	$-0.129$	$[-0.213,\ -0.030]$
Group	Budget-Shuffled-AS95	$-0.196$	$[-0.362,\ -0.083]$
Temporal	Budget-Shuffled-AS95	$-0.367$	$[-0.575,\ -0.109]$

Appendix

Table 3: Paired AS-VQC-95 decision-disagreement differences with percentile 95% bootstrap intervals over five seed-level differences. Values are percentage points.

Explore similar work

May 19, 2026cs.CR

Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets

Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive anomaly detectors. We present a leakage-free evaluation of quantum machine learning for UAV anomaly detection on the multi-sensor TLM:UAV benchmark. Three contributions support the study. (i) A group-aware temporal protocol (B2) partitions the dataset into ten contiguous TimeUS blocks and evaluates over ten seeds, eliminating the inflation produced by random stratified splits that mix neighbouring samples. (ii) A three-mode feature audit (full/loose/strict) quantifies how much accuracy stems from instantaneous physical signals versus contextual proxies (cumulative energy, battery state, GPS trajectory). (iii) A hybrid XGBoost + Data Reuploading (DRU) classifier is benchmarked against five paired non-linear controls (raw, PCA, polynomial-2, random-RBF, and an untrained DRU map) under identical budgets. The standalone DRU does not consistently match the strongest classical baseline across seeds; however, the trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05), a directional signal that the per-seed standard deviations prevent from being interpreted as a statistically established difference. The trained-DRU hybrid also records the lowest mean false-alarm rate under proxy-free evaluation, subject to the inter-seed variance reported. We frame this as an incremental, reproducible quantum-enhanced hybrid benefit, and provide an open Qiskit 2.x implementation as a benchmark for cybersecurity analytics in NISQ-era aerospace systems.

Carlos A. Durán Paredes, Javier E. León Calderón, Nicolás Sánchez Perea +2

Corporation for Aerospace Initiatives, Research and Innovation (CASIRI), Popayán, Colombia · Department of Electronics Engineering, Universidad Nacional de Colombia, Manizales, Colombia. · Department of Electronics Engineering, Universidad del Cauca, Popayán, Colombia. +1

Drone Detection Quantum Machine Learning Unmanned Aerial Vehicles Multi-Modal Sensing Model Auditing

Ask PDF

Jul 15, 2026cs.LG

RF Spectrogram Anomaly Detection with Quantum Kitchen Sinks: Architecture, Representation, and Hardware Validation

The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management. Quantum Kitchen Sinks (QKS) offer a lightweight hybrid quantum feature map suitable for near-term quantum devices, yet their behavior on structured signal data remains poorly understood. In this paper, we extend the standard QKS template with multi-depth data re-uploading and ring entanglement, and evaluate the resulting pipeline on controlled RF spectrogram anomaly detection. We introduce a validation-locked five-stage ablation protocol that systematically separates the effects of shallow architecture, re-uploading depth, episode budget, input representation, and classical readout. Across the completed benchmark, Discrete Cosine Transform (DCT) representations consistently dominate raw and Principal Component Analysis (PCA) inputs, moderate-depth entangled QKS configurations form the strongest operating regime, and QKS improves over matched classical direct-readout baselines across all evaluated representation-readout pairs on the held-out test set, with the best configuration reaching a test Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8778 and a test F1 of 0.7995. The study bridges two levels of realism: real measured sub-6,GHz cellular signals on the data side and real-device validation on the ibm_quebec Quantum Processing Unit (QPU) on the computing side, with AUROC deviations below 0.013 relative to simulation. These results provide a practical, reproducible framework for deploying QKS-based anomaly detection in wireless networks.

Abdallah Aaraba, Alexis Vieloszynski, Remon Polus +2

Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada · Thales cortAIx Labs, Montréal, QC, Canada

Quantum Kernels Quantum Processor Entanglement

Ask PDF

Jun 8, 2026quant-ph

Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution

Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time. Yet a filtered controller that never violates a constraint may still have learned nothing about safety: the filter can silently repair an incompetent upstream policy, so that post-filter success measures the filter, not the policy. We argue that safe policy learning should ask who earns the safety - the policy or its protective layers - and we make this question measurable. We introduce Intervention-Aware Variational Quantum Differentiable Predictive Control (IA-VQC-DPC), which (i) trains a compact variational quantum circuit (VQC) policy under a primal-dual intervention budget that penalizes reliance on a differentiable Control-Barrier-Function (CBF) projection, and (ii) is evaluated with a safety-attribution protocol that decomposes the executed-trajectory correction into a CBF term and a deployment runtime-guard term, and stress-tests the policy with guard-off evaluation. On closed-loop, high-fidelity BOPTEST building-control emulators (5 seeds, 60 episodes per method), intervention-aware training significantly lowers the quantum policy's raw pre-filter violation and total safety-layer reliance (both p < 10^-4) with no significant energy regression; at an equal approximately 400-parameter budget the quantum policy is significantly safer and more comfortable than a matched classical policy. Guard-off evaluation confirms the improvement is policy-level and exposes a valuable negative result: a learned differentiable energy head is only safe when paired with a distribution-aware runtime guard. The attribution protocol is general beyond quantum policies and buildings.

Yifan Wang

Department of Mechanical Engineering, McGill University Montréal, QC H3A 2T7, Canada

Safety Filters Model Predictive Control Emulators

Ask PDF