Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911289833422848 |
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| author | Chandrasekhar, Swathi Pokhrel, Shiva Raj Kumari, Swati Singh, Navneet |
| author_facet | Chandrasekhar, Swathi Pokhrel, Shiva Raj Kumari, Swati Singh, Navneet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection Chandrasekhar, Swathi Pokhrel, Shiva Raj Kumari, Swati Singh, Navneet Machine Learning Quantum Physics 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. |
| title | Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2511.21932 |