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:2608. 18572v1 Announce Type: cross Abstract: Tactile Internet services couple cyber events directly to physical actuation, so security decisions must improve risk discrimination without perturbing the control path.
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.
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs.
arXiv:2607. 16276v1 Announce Type: cross Abstract: Quantum-repeater networks require adaptive control policies that balance entanglement generation rate, end-to-end fidelity, purification overhead, and memory-induced latency.
arXiv:2607. 11843v1 Announce Type: cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood.
arXiv:2605.25066v2 Announce Type: replace-cross Abstract: As machine-learning workloads move onto quantum hardware they inherit a trust problem classical AI deployments have largely solved and quantu...
arXiv:2606. 09778v1 Announce Type: cross Abstract: Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time.
arXiv:2608. 15617v1 Announce Type: new Abstract: Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
The paper investigates the role of quantum computing in network‑security classification through two experiments. First, it evaluates near‑term quantum‑kernel support vector machines on datasets such as KDD Cup 1999, CICIDS2017, and BoT‑IoT, finding that quantum kernels can match or sometimes improve classical baselines, though classical RBF kernels often remain stronger. Second, it explores long‑term memory efficiency using quantum oracle sketching (QOS), showing that quantum methods can achieve comparable accuracy with a smaller effective memory footprint than explicit storage, suggesting a potential advantage in memory‑efficient data access for streaming classification tasks.
arXiv:2607. 05307v1 Announce Type: cross Abstract: A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal.
The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.