arXiv:2608. 20240v1 Announce Type: new Abstract: X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence.
arXiv:2608. 04047v1 Announce Type: cross Abstract: Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping.
arXiv:2604. 10933v2 Announce Type: replace-cross Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications.
The paper introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network that learns coefficients over a finite Fourier series support similar to quantum neural networks (QNNs). By testing on tabular benchmark datasets, NFS demonstrates competitive classification performance against established classical baselines and data‑reuploading QNNs. The authors also compare the learned Fourier spectra of QNNs and NFS on synthetic data, positioning NFS as a natural classical baseline for evaluating QNN performance.
The paper investigates whether noise in quantum hardware limits the performance of quantum kernel methods for network intrusion detection. Using Pegasos-QSVC on the NSL-KDD and ToN-IoT datasets, the authors run 56 experiments across noiseless and noisy backends, finding that quantum models achieve lower accuracies than classical XGBoost and exhibit high seed-dependent variability, including degenerate classifiers. Kernel Gram matrix analysis shows low effective rank and severe ill-conditioning even without noise, indicating intrinsic fragility in the quantum representation.
arXiv:2607. 13722v1 Announce Type: cross Abstract: The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers.
arXiv:2607. 14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference.
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:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.
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:2510. 24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability.
arXiv:2607. 13897v1 Announce Type: new Abstract: The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management.