AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Quantum Neural Network (QNN) provides an exciting alternative to machine learning as they take advantage of quantum computer properties when making decisions and processing data. However, the integration of high-dimensional classical data with the limited number of qubits available on current Noisy Intermediate-Scale Q...
The practical value of quantum machine learning for network anomaly detection remains unclear under controlled and comparable benchmarking conditions. This study evaluates a trash-qubit-based Quantum Autoencoder (QAE) against seven classical anomaly-detection methods on UNSW-NB15 using shared data partitions, preproces...
The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or ad...
Under tight feature and compute budgets, classical threat detection pipelines often degrade on near-decision-boundary events. Small quantum processors are now available, but existing work inadequately shows whether quantum components improve end-to-end threat detection under the above resource constrained conditions. T...
This study develops and evaluates a resource-aware hybrid quantum-classical intrusion detection framework that combines fixed fidelity quantum kernels, repaired Quantum Kernel Alignment, a Variational Quantum Classifier, LightGBM, prototype-based kernel reduction, and probabilistic stacking.
Quantum machine learning (QML) is an emerging research area that combines quantum computing with machine learning to exploit quantum superposition, entanglement, interference, and high-dimensional Hilbert-space representations. This paper presents a concise survey of QML models and algorithms for near-term noisy interm...
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