Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection

• URL: http://arxiv.org/abs/2511.21932v1
• Date: Wed, 26 Nov 2025 21:45:02 GMT
• Title: Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
• Authors: Swathi Chandrasekhar, Shiva Raj Pokhrel, Swati Kumari, Navneet Singh,
• Abstract summary: We present a quantum autoencoder framework that compresses network traffic into latent representations and employs quantum support vector classification for intrusion detection.<n>This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.
• Score: 5.822890076771093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.

Related papers

• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Cyber Threat Detection Enabled by Quantum Computing [7.119884090052995]
  Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware.<n>Small, near-term quantum processors are now available, but existing work rarely shows whether quantum components can improve end-to-end detection.<n>We address this gap with a hybrid architecture that uses a compact multilayer perceptron to compress security data and then routes a few features to 2-4 qubit quantum heads.
  arXiv  Detail & Related papers  (2025-12-20T20:10:36Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses [2.089191490381739]
  Quantum Machine Learning (QML) integrates quantum computing with classical machine learning to solve classification, regression and generative tasks.<n>This chapter examines adversarial threats unique to QML systems, focusing on vulnerabilities in cloud-based deployments, hybrid architectures, and quantum generative models.
  arXiv  Detail & Related papers  (2025-06-27T01:19:49Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• Quantum-driven Zero Trust Framework with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach [0.0]
  We propose the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF) for enhanced security.<n>We integrate Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) for enhanced security.<n>We show improved cyber threat mitigation, demonstrating the framework's effectiveness in reducing false positives and response times.
  arXiv  Detail & Related papers  (2025-02-11T18:59:32Z)
• Learning agent-based approach to the characterization of open quantum systems [0.08496348835248901]
  We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism.<n>By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system.<n>We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors.
  arXiv  Detail & Related papers  (2025-01-09T16:25:17Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.