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Quantum key distribution (QKD) is widely regarded as a highly secure quantum encryption approach since it applies theoretical principles of quantum mechanics as a medium of security, unlike classical encryption processes that rely on complex mathematics. However, practical QKD systems are vulnerable due to imperfect photon source hardware, communication channel, and detector implementation. These hardware imperfections open side channels that an adversary can exploit without violating principles of quantum mechanics. This review provides a comprehensive overview of the current QKD protocols, quantum hacking strategies and their operating principles, vulnerable hardware components and their mitigation techniques. This paper further investigates how state-of-the-art paradigms based on artificial intelligence (AI) and machine learning (ML), along with key subfields—such as deep learning (DL), deep reinforcement learning (DRL), and quantum machine learning (QML)—are reshaping the domain. In particular, this paper analyzes their dual roles as enablers of sophisticated attacks and as instruments for attack detection and mitigation. By jointly addressing the challenges in quantum communication and adaptive learning, as well as attack and detection architectures, this survey elucidates both the capabilities and the limitations of contemporary attack-detection mechanisms in the field. This survey seeks to establish a unified framework that conceptually integrates physical-layer quantum hacking with AI-/ML-based security, emphasizing their dual function as both an intelligent defensive layer and a potential tool for advanced threats.

Underwater visible light communication (UVLC) offers high data rates over acoustic and radio-frequency links but is highly sensitive to bubble-induced scattering. This paper presents an RGB wavelength-division-multiplexed UVLC system simulated in OptiSystem across clear-water, intermediate, and bubble-induced turbidity conditions. Eye-diagram features are classified using XGBoost, achieving 98.75% test accuracy and 96.18% (±2.17%) repeated cross-validation accuracy, statistically validated against SVM and Random Forest. A held-out intermediate-turbidity set, unseen during training, is correctly identified by wavelength with 92.31–70.65% accuracy across 25–75% turbidity. An independently computed Mie scattering calculation shows bubble-induced scattering becomes wavelength-insensitive at realistic bubble sizes, consistent with the classifier’s confusion pattern. A systematic grid search over laser bias and modulation current on the blue channel identifies a distinct optimal operating point for clear-water and bubble-induced conditions, with bubble-induced BER reaching $6.95\times {10}^{-9}$ at its optimum, and indicates a sharp, condition-dependent transition in which settings tolerated under clear water drive the bubble-induced link toward its estimated reliability limit. Shannon capacity analysis indicates multi-gigabit-per-second headroom, and comparison with acoustic and radio-frequency alternatives confirms UVLC’s data-rate advantage at short range. The framework connects physical-layer simulation, classification, and characterized adaptive control within a single validated system.

Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window . Global phase metrics remain heavily suppressed regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging.