Abstract As the number of users in quantum key distribution (QKD) networks continues to grow, parameter selection (e.g., probability of X-bases, signal state strength, and decoy state levels) has become increasingly complex. Traditional methods such as local search algorithms (LSA) are commonly employed for parameter optimization but suffer from significant computational overhead, slow execution on low-power platforms, and resultant system delays. These limitations hinder the scalability and real-time requirements of large-scale QKD networks. To address these challenges, this paper proposes an innovative approach that combines Bayesian optimization with XGBoost model to optimize QKD system parameters. The proposed model is evaluated against random forest (RF) and back-propagation neural network (BPNN) models based on three critical metrics: prediction accuracy, model fitting, and training efficiency. Simulation results demonstrate that the proposed model significantly outperforms both RF and BPNN in terms of accuracy and computational efficiency. Notably, while achieving predictive performance comparable to LSA, the proposed method reduces computation time by three orders of magnitude. This research introduces a novel and efficient framework for QKD parameter optimization, providing new insights and directions for enhancing the performance, scalability, and real-time adaptability of QKD systems.
Abstract As the number of users in quantum key distribution (QKD) networks continues to grow, parameter selection (e.g., probability of X-bases, signal state strength, and decoy state levels) has become increasingly complex. Traditional methods such as local search algorithms (LSA) are commonly employed for parameter optimization but suffer from significant computational overhead, slow execution on low-power platforms, and resultant system delays. These limitations hinder the scalability and real-time requirements of large-scale QKD networks. To address these challenges, this paper proposes an innovative approach that combines Bayesian optimization with XGBoost model to optimize QKD system parameters. The proposed model is evaluated against random forest (RF) and back-propagation neural network (BPNN) models based on three critical metrics: prediction accuracy, model fitting, and training efficiency. Simulation results demonstrate that the proposed model significantly outperforms both RF and BPNN in terms of accuracy and computational efficiency. Notably, while achieving predictive performance comparable to LSA, the proposed method reduces computation time by three orders of magnitude. This research introduces a novel and efficient framework for QKD parameter optimization, providing new insights and directions for enhancing the performance, scalability, and real-time adaptability of QKD systems.
The mode-pairing quantum key distribution (MP-QKD) achieves high key generation rates and high security over long-distance, low-loss optical fibers through its mode-pairing technique. In practical applications, when considering finite data sets, MP-QKD requires optimization of parameters such as channel optical bit error rate ( e opt ), detector’s dark count( P d ), the total number of signals( N ), and the number of phase slices( M ). However, traditional exhaustive traversal or local search algorithms do not meet the time and resource requirements of real-time communication systems. Combined with machine learning, the quantum key distribution for parameter optimization prediction has become the mainstream approach. XGBoost, a gradient boosting decision tree (GBDT) algorithm, random forest (RF) representing a classic bagging-based ensemble learning method, and back-propagation neural networks (BPNN) are important algorithms in neural networks. This study employs the LightGBM (light gradient boosting machine) algorithm to predict optimized parameters for MP-QKD. The results demonstrate that LightGBM efficiently and accurately predicts optimal parameters, slightly outperforming XGBoost, RF, and BPNN in parameter prediction, and can serve as a reference for future real-time QKD networks.
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.
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.
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.
Selection of parameters (e.g., the probability of choosing an X-basis or Z-basis, the intensity of signal state and decoy state, etc.) and system calibrating are more challenging when the number of users of a measurement-device-independent quantum key distribution (MDI-QKD) network increases. At present, optimization algorithms are usually employed when searching for the best parameters. This method can find the optimized parameters accurately, but it may take a lot of time and hardware resources. This is a big problem in a large-scale MDI-QKD network. Here, we present, to the best of our knowledge, a new method, using a back propagation artificial neural network (BPNN) to predict, rather than search, the optimized parameters. Compared to optimization algorithms, our BPNN is faster and more lightweight, and it can save system resources. Another big problem brought by large-scale MDI-QKD networks is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from the communication process, rather than adding additional devices or scanning the system.
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.