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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.
arXiv:2606. 13422v2 Announce Type: replace-cross Abstract: We develop theoretical foundations for a practical quantum-advantage mechanism in quantum-informed machine learning for chaotic dynamical systems.
arXiv:2607. 02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned.
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."
arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.
arXiv:2607. 27859v1 Announce Type: cross Abstract: Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations.