The advent of quantum computing is bringing threats to successful operations of classical cryptographic techniques. To conduct quantum key distribution (QKD) in a finite time interval, there is a need to estimate photon states and analyze the fluctuations statistically. The use of brute force and local search methods for parameter optimization are computationally intensive and becomes an infeasible solution even for smaller connections. Therefore, the use of quantum machine learning models with self-learning ability is useful in predicting the optimal parameters for quantum key distribution. This chapter discusses some of the quantum machine learning models with their architecture, advantages, and disadvantages. The performance of quantum convoluted neural network (QCNN) and Quantum Particle Swarm Optimization (QPSO) towards QKD is found to be good compared to all the other quantum machine learning models discussed.
The advent of quantum computing is bringing threats to successful operations of classical cryptographic techniques. To conduct quantum key distribution (QKD) in a finite time interval, there is a need to estimate photon states and analyze the fluctuations statistically. The use of brute force and local search methods for parameter optimization are computationally intensive and becomes an infeasible solution even for smaller connections. Therefore, the use of quantum machine learning models with self-learning ability is useful in predicting the optimal parameters for quantum key distribution. This chapter discusses some of the quantum machine learning models with their architecture, advantages, and disadvantages. The performance of quantum convoluted neural network (QCNN) and Quantum Particle Swarm Optimization (QPSO) towards QKD is found to be good compared to all the other quantum machine learning models discussed.
The advent of quantum computing is bringing threats to successful operations of classical cryptographic techniques. To conduct quantum key distribution (QKD) in a finite time interval, there is a need to estimate photon states and analyze the fluctuations statistically. The use of brute force and local search methods for parameter optimization are computationally intensive and becomes an infeasible solution even for smaller connections. Therefore, the use of quantum machine learning models with self-learning ability is useful in predicting the optimal parameters for quantum key distribution. This chapter discusses some of the quantum machine learning models with their architecture, advantages, and disadvantages. The performance of quantum convoluted neural network (QCNN) and Quantum Particle Swarm Optimization (QPSO) towards QKD is found to be good compared to all the other quantum machine learning models discussed.
For a practical quantum key distribution (QKD) system, parameter optimization, the choice of intensities and the probabilities of sending them, is a crucial step in gaining optimal performance, especially when one realistically considers a finite communication time. With the increasing interest in the field to implement QKD over free space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the internet of things, a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 W, and we find a speedup of up to two to four orders of magnitude when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve, e.g., over 95%--99% of the optimal secure key rate for a given protocol. Moreover, our approach is highly general and can be applied effectively to various kinds of common QKD protocols.
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.
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.
Abstract Quantum key distribution (QKD) is a pivotal quantum communication technology but faces practical challenges such as transmission loss and measurement device vulnerabilities. Mode-pairing QKD (MP-QKD) addresses these limitations by dynamically pairing single-photon detection events, enabling secure long-distance communication with simplified experimental requirements. However, optimizing key parameters like pulse intensity remains computationally demanding for traditional numerical methods. To overcome this, this paper shows a machine learning framework that combines Particle swarm optimization (PSO) and three neural networks—Backpropagation neural network (BPNN), XGBoost, and Long short-term memory (LSTM)—to predict optimal pulse intensity under varying environmental conditions. Using PSO-generated datasets, the models map channel loss, error rates, transmission distance, and other parameters to pulse intensity. Simulations demonstrate that BPNN achieves 99.87% prediction accuracy (RMSE: 0.0026) with an average runtime of 13 milliseconds per prediction, outperforming XGBoost and LSTM in both speed and precision. This work establishes a scalable solution for real-time parameter optimization in large-scale QKD networks and highlights the transformative role of machine learning in advancing quantum communication technologies.
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.