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.
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.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.
With the considerable progress of sixth-generation (6G) networks further on the horizon, security has attracted much attention in the face of increasingly sophisticated threats, especially from quantum computing. Classical cryptographic schemes based on computational hardness are becoming more and more brittle, as they can easily be attacked with a quantum computer. Quantum Key Distribution (QKD) arises as a promising tool for information-theoretic security through quantum mechanics to obtain an unbreakable key by exchanging unknown bit sequences. In this paper, we provide an extensive survey on the integration of QKD both in the envisioned 6G architecture, including current implementations, technical feasibility, and deployment of QKD in fiber-based and wireless scenarios. We compare performance metrics of QKD systems in simulating 6G systems such as secret key rates, quantum bit error rates, and robustness to noise and mobility. We propose and investigate hybrid security schemes to leverage both classical PQC and QKD technologies to build multilayered security shields. We highlight the importance of the Artificial Intelligence (AI) and Machine Learning (ML) in improving QKD systems in terms of smart error correction, dynamic routing, anomaly detection, and adaptive key management, and more. In spite of efforts, the paper points out important research challenges that remain to be addressed in scalability, standardization, cross-operation comparison and demonstration. We finally summarize new directions for quantum-secured networking, offering a future research agenda which combines AI, standardization works and real testbeds to deliver a QKD shift from a lab-scale proof of concept to global-scale 6G network adoption. This review serves as a useful reference for researchers and practitioners as they develop quantum-safe, next-generation communication networks.
Quantum Key Distribution (QKD) provides key exchange security grounded in quantum physics, but practical deployments still face constraints in scalability, noise tolerance, real-time operations, and implementation security. In parallel, machine learning (ML) has proven effective for adaptive control, prediction, and anomaly detection in complex cyber-physical systems. This paper reviews how AI/ML can be integrated into QKD to improve robustness and operational security without weakening core cryptographic guarantees. We present an AI-supported QKD architecture, summarize key performance metrics (QBER, secret key rate, and throughput), and outline how learning-based control can tune parameters dynamically under varying channel conditions. We also discuss security-operations integration (SOC visibility, alerting, and key lifecycle telemetry) and provide a reinforcement learning (RL) case study to illustrate adaptive parameter selection. Finally, we analyze threats introduced by AI and identify practical design constraints (fail-safe behavior, policy bounds, and verification) required to preserve quantum-security principles while enabling quantum-safe integration with post-quantum cryptography workflows.
Abstract The increasing demand for secure communications in mobile and critical infrastructure networks has led to the emergence of Quantum Key Distribution (QKD) as a promising technique for enhancing cryptographic security in next-generation systems. This paper reviews recent progress in integrating QKD within mobile and wireless networks. It focuses on key enabling technologies, such as Software-Defined Networking (SDN), satellite-based, and aerial networks. The paper critically analyzes how these frameworks can address evolving security requirements in sixth-generation (6G) networks and smart city infrastructures. Additionally, it examines the role of Machine Learning (ML) in improving QKD performance and adaptability. Challenges such as key management, mobility support, scalability, and interoperability are explored. These challenges help identify current barriers and research directions. By consolidating developments and highlighting unresolved issues, this study offers researchers valuable insights. It discusses the convergence of quantum communication and advanced mobile architectures, facilitating the design of secure and resilient future communication infrastructures.
Quantum Key Distribution (QKD) enables information-theoretic secure key exchange by exploiting fundamental quantum phenomena such as superposition and no-cloning. Classical QKD protocols (e.g., BB84) guarantee that any eavesdropping attempt introduces detectable disturbances, but they typically assume stable, high-quality channels and unconstrained devices. In contrast, Internet of Things (IoT) networks present a radically different environment: devices have limited processing power, minimal onboard memory, strict energy budgets, and communicate over highly variable wireless links subject to interference and rapid fading. These constraints lead to elevated quantum bit error rates (QBER), frequent key reconciliation failures, and prohibitive energy costs, all of which jeopardize practical QKD deployment. To address these challenges, we propose an AI-powered QKD protocol specifically optimized for IoT scenarios. Our approach integrates three AI modules: (1) a gradient-boosted regression channel estimator that predicts instantaneous link transmittance using lightweight sensor data and historical photon counts; (2) a reinforcement learning (RL) agent that adaptively tunes photon intensity, basis-selection probability, and reconciliation block size to balance key rate and error rate; and (3) a convolutional neural network (CNN) classifier that selects the optimal Low-Density Parity-Check (LDPC) code rate based on real-time noise characteristics. We simulate a star-topology IoT network of 50 battery-powered devices communicating over 5 km polarization-encoded links with realistic urban loss (0.1–0.3 dB/km) and environmental noise. Compared against a static BB84 baseline, our AI-enhanced protocol reduces average QBER from 4.5% to 2.9%, increases secure key rate from 12.5 kbps to 16.0 kbps, lowers latency by 22%, and reduces energy consumption per key bit by 18%. These improvements persist across channel conditions and device heterogeneity, demonstrating robustness.
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.