arXiv subjects

Hasan Abbas Al-Mohammed

Publications and source records attributed to Hasan Abbas Al-Mohammed.

4 recordsLinked to original sources

arXiv2026

From Received Power to Certified Secret Keys: A General Method for Bridging Classical FSO Link Budgets and Decoy-State QKD

Received optical power determines photon arrival flux, but photon arrival flux does not certify a quantum key. This paper gives a deployment-independent interface from a classical free-space optical (FSO) link budget to phase-randomized weak coherent pulse BB84, decoy-state estimation, and composable finite-key postprocessing. The channel interface separates geometric collection, atmospheric extinction, residual pointing, and detector efficiency, and states when common geometric and pointing expressions are approximations. The finite-key output follows the decoy-state analysis of Lim et al., with key- and test-basis counts estimated separately and an infinite-decoy asymptotic reference. The numerical study covers a five-level attenuation sweep, basis- and vacuum-probability sensitivities, a conditional Chernoff comparison, and integer-count Monte Carlo. For $3.33 \times 10^8$ emitted pulses, the Hoeffding-based predicted one-kilobit-per-second distance limits are 2.727, 1.883, 1.472, 1.236, and 0.962 km from very clear to dense-fog parameterizations. These are conditional performance predictions, not experimental security certificates. Satellite, drone, high-altitude-platform, terrestrial, and train links enter through the same interface but require their own propagation, acquisition, and device inputs. The framework integrates established security tools into an engineering procedure without claiming a new security proof or a universal range.

quant-ph↗

arXiv2026

Machine Learning for Specialized QKD Aspects: A Survey of Adaptive Protocols, Free-Space Links, 6G Integration, and Steerability-Aware Security

Quantum Key Distribution (QKD) provides information-theoretic security grounded in the laws of quantum mechanics, yet practical deployment increasingly extends beyond conventional point-to-point fiber links. Several rapidly emerging QKD directions are often studied separately, including adaptive protocol and parameter support; free-space, satellite, UAV, and high-altitude platform (HAP) channels; integration with IoT and 6G networks; quantum-secured federated learning; Quantum Machine Learning (QML) assisted decision support; and steerability-aware estimation for one-sided device-independent QKD. This survey examines how Machine Learning (ML), Reinforcement Learning (RL), and QML address these specialized scenarios and organizes the literature into five thematic pillars: (I) adaptive protocol and parameter support; (II) free-space, satellite, UAV, and HAP-assisted QKD; (III) QKD for IoT, 6G, and quantum-secured federated learning; (IV) QML-assisted QKD functions; and (V) steerability-aware and one-sided device-independent QKD security estimation. For each theme, we follow a consistent problem, conventional solution, and ML/RL/QML solution structure and summarize reported gains using metrics such as accuracy, mean absolute percentage error, QBER reduction, and secret key rate improvement. We further provide thematic and cross-theme comparison tables and identify open challenges, including dataset scarcity, transferability across weather and mobility conditions, interpretability, trustworthy QML, and the boundary between ML-based decision support and security certification. This survey serves as a focused reference for adaptive, non-terrestrial, and application-integrated QKD systems.

quant-ph↗

arXiv2026

From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution

Quantum key distribution (QKD) promises information-theoretic security, yet practical deployments in discrete-variable (DV) and continuous-variable (CV) settings remain exposed to device imperfections, channel manipulation, finite-key effects, and vulnerabilities in machine-learning (ML) components used for adaptation and monitoring. This survey adopts a problem-driven perspective based on nine practical problem classes (P1-P9) spanning device, channel, protocol, ML, and network layers. For each class, we compare classical defenses with ML-enabled solutions including anomaly detection, parameter prediction, noise estimation, adversarial purification, and resource allocation. Reported results include DBSCAN-based CV attack detection at P=99.7%, R=99.8%, F1=0.998, adversarial robustness recovery up to 79.5%, channel-amplification detection at 100%/91.26% under low/high-noise conditions, and LightGBM-based noise prediction reducing evaluation time by up to 98.8%. The survey further proposes a benchmarking framework combining datasets, stress protocols, and unified evaluation metrics including SKR impact, maximum distance, latency, and robustness. Finally, we provide defense-in-depth deployment guidelines and outline future research directions for secure and practical QKD systems.

quant-ph↗

arXiv2024

Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.

quant-ph↗