VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Cryptography and Security
arXiv:2608.18572 (cs)
[Submitted on 19 Aug 2026]
Title:VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
Authors:Mubassir Serneabat Sudipto (Iowa State University), Shakil Ahmed (Grand Valley State University), Ashfaq Khokhar (Kansas State University)
View a PDF of the paper titled VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet, by Mubassir Serneabat Sudipto (Iowa State University) and 2 other authors
View PDF
HTML (experimental)
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.
| Comments: | 7 pages, 3 figures, 3 tables. Accepted at IEEE Global Communications Conference (GLOBECOM 2026) |
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2608.18572 [cs.CR] |
| (or arXiv:2608.18572v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18572
arXiv-issued DOI via DataCite
|
Submission history
From: Mubassir Serneabat Sudipto [view email][v1] Wed, 19 Aug 2026 06:08:41 UTC (219 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
View a PDF of the paper titled VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet, by Mubassir Serneabat Sudipto (Iowa State University) and 2 other authors
Current browse context:
cs.CR
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
SLM-Conditioned Hierarchical Relation Routing for Labeled Property Graph Learning
Aug 28
-
NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation
Aug 28
-
Pruning Binarized Neural Networks: A Dedicated Framework and Globally Weighted Algorithms
Aug 28
-
Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization
Aug 28
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.