Hugging Face Trending Papers

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

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The paper introduces the Adaptive‑Shot Variational Quantum Circuit (AS‑VQC) policy for finite‑shot hybrid quantum anomaly detection in Tactile Internet security. Starting with 128 shots per record, the policy escalates to 256, 512, and 1024 shots only when the anomaly score is near a validation‑selected threshold, thereby reducing computational load while maintaining decision accuracy. Experiments on a 4,875‑record benchmark show that the AS‑VQC‑95 policy averages roughly 130–280 shots per record, saving 73–87% of the resources compared to a fixed 1024‑shot baseline, with decision disagreement below 1%. whyItMatters":"The study demonstrates that adaptive quantum measurement allocation can significantly cut resource usage in security analytics without compromising reliability, offering a practical trade‑off for constrained Tactile Internet environments."

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arXiv AI
Aug 20

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

The study evaluates quantum machine learning (QML) models for network intrusion detection against well-tuned classical baselines across four standard datasets, using a leakage-controlled protocol and noise simulation. It introduces a quantum-attribution audit to determine whether any performance gains are truly due to quantum effects. While most tuned classical models match or surpass QML, two quantum approaches— a quantum-kernel SVM and a small hybrid circuit—show statistically significant advantages on specific metrics and tasks.

By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui