A reliable secure communication can be given between two remote parties by key sharing, quantum key distribution (QKD) is widely concentrated as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques that deal with quantum key distribution network (QKDN), however, only few of them use machine learning (ML) and soft computing techniques to improve QKDN. ML can analyze data and improve itself through model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of ML technologies. Given ML’s advantageous features, it can help improve and resolve issues in QKDN, facilitating its commercialization. The proposed work provides a detailed understanding of role of each layer of QKDN, addressing the limitations of each layer, and suggesting a framework to improve the performance metrics for various applications of QKDN by applying machine learning techniques, such as support vector machine and decision tree algorithms.
A reliable secure communication can be given between two remote parties by key sharing, quantum key distribution (QKD) is widely concentrated as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques that deal with quantum key distribution network (QKDN), however, only few of them use machine learning (ML) and soft computing techniques to improve QKDN. ML can analyze data and improve itself through model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of ML technologies. Given ML’s advantageous features, it can help improve and resolve issues in QKDN, facilitating its commercialization. The proposed work provides a detailed understanding of role of each layer of QKDN, addressing the limitations of each layer, and suggesting a framework to improve the performance metrics for various applications of QKDN by applying machine learning techniques, such as support vector machine and decision tree algorithms.
Quantum key distribution (QKD) technology can achieve information theoretical secure with one-time pad encryption. Machine learning (ML) provides systems the ability to automatically learn and improve from experience without being explicitly programmed. There are some applications of ML in telecommunication networks, such as traffic prediction and fault prediction. There is increasing interest in the field in applying ML to improve the performance of quantum communication networks. In this talk, I will introduce the applications of machine learning (ML) in quantum key distribution networks (QKDNs), including the applications in quantum layer, key management layer, and control and management layers.
Purpose The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD. Design/methodology/approach The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN). Findings Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection. Originality/value The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
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.
Quantum Key Distribution (QKD) promises theoretically unbreakable encryption by using fundamental quantum physics principles. QKD has emerged as a contender in the next generation of encryption protocols as the demand for more secure communication grows in our linked society. However, practical QKD implementations confront obstacles such as noise, interception attempts, and flaws in quantum hardware. We investigate the possibilities of Machine Learning (ML) techniques to improve the efficiency, resilience, and security of QKD protocols in this study. We provide a novel system that uses deep learning models to optimize photon polarization states and hence increase the key generation rate. Our approach detects and mitigates eavesdropping attempts using unsupervised learning algorithms, substantially enhancing the security of quantum communications. To achieve security, the BB84 protocol makes use of fundamental quantum mechanics features. Furthermore, we use reinforcement learning to adaptively alter system settings in real time to account for the quirks and noise profiles of different quantum channels. In comparison to typical QKD setups, experimental results show that our ML-enhanced QKD system is more resilient to known quantum attacks and provides a more consistent and efficient key distribution rate. Our findings indicate that the interaction of quantum physics and machine learning can pave the way for more secure and practical quantum communication networks in the near future.
In the future massive applications of quantum communication network, it is crucial to realize real-time selection of the optimal quantum key distributions (QKD) protocol for the improvement of system security and optimizing resources configuration. In principle, this can be done by utilizing algorithms such as exhaustive traversal or local search algorithm, whose time cost, however, is unbearable. Here we for the first time propose to employ machine learning methods into the selecting of optimal QKD protocol and apply random forest (RF) as an example for illustration. With the help of the easy-to-train classifier of RF, we can achieve a highly efficient optimal QKD protocols selector. Besides, we also do comparisons among RF and other machine learning methods, e.g., supporting vector machine, K-nearest neighbors algorithm, multinomial naive bayes classifier, and convolutional neural networks. Results demonstrate that, besides its advantage in efficiency, the RF classifier also excels both in preciseness and robustness with an accuracy over 98% for the testing set and an enjoyable receiver operating characteristic.
Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols plays a crucial role in enhancing security measures, addressing previously inaccessible threats, and improving cryptographic efficiency. Key research areas in quantum cryptography include Quantum Key Distribution (QKD), eavesdropping detection, QSDC, security analysis of QKD protocols, post-quantum cryptography, Quantum Network Security & Intrusion Detection, Quantum-secure communication beyond QKD, quantum random number generation, Quantum Secure Multi-Party Computation (QSMPC), Quantum Homomorphic Encryption (QHE), and privacy-preserving computation. QML algorithms improve the key generation of QKD, by improving quantum state selection and reducing measurements. This also allows them to increase efficiency because it identifies trends in errors and applies corrections, making quantum cryptography a more dependable option. With intelligent processing machine learning is excellent at handling complex, high-dimensional data-this may provide a viable strategy for enhancing QKD performance and increasingly real-world secure quantum communication networks. This review will explore current research gaps and future developments in QKD, security analysis of QKD protocols, and eavesdropping detection by leveraging various QML algorithms.