A Survey and Comparison of Some of the Most Prominent QKD Protocols

Rainu Nandal, Ashish Nandal

Srinivasan Arunachalam

Quantum key distribution (QKD) is one of the best-known examples of an application of quantum mechanics to cryptography.This article serves as a resource letter, a brief description to the introduction of QKD is provided before surveying the most prominent QKD protocols present in the literature from theoretical initialization by Wiesner to the attempts at practical implementations.We have also given an overview of the different security proofs proposed, for the variations in protocols and highlighted their significance.

Daniel J. Rogers

Neha Sharma, Vikas Saxena

A. B. Manju, Akoramurthy Balasubramaniam, J. Jegan +2 more

Significant advancements in quantum key distribution (QKD) networks are transforming theoretical ideas into real-world applications. Conventional key exchange protocols rely on mathematical models, making key guessing methods time-consuming and irrational. The strength of these methods depends solely on the exchange procedure. Quantum key exchange methods overcome the mentioned issues by integrating quantum mechanics, which serve as replacements for conventional key exchange methods. The research aims to explore QKD and its applications. QKD protocols namely BB84, E91, and BBM92 are considered for comparison and analysis of their applicability in various network contexts and performance metrics. A detailed study examines the interfaces, negotiation protocols, and all requirements for secure key exchange methodologies. This paper surveys and analyses existing quantum key exchange methods and explores possibilities to enhance QKD protocols. QuNetSim is used for simulation the QKD protocols.

Hasan Abbas Al-Mohammed, Afnan S. Al-Ali

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.