Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1812.07724 · Physical Review A

Machine learning for optimal parameter prediction in quantum key distribution

Wenyuan Wang, Hoi‐Kwong Lo

Published Dec 27, 2019• 76 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1103/physreva.100.062334

Related papers

Machine learning for optimal parameter prediction in free space continuous-variable quantum key distribution

Chen Dong, Cheng Jin, Ao Liu +4 more

Abstract For a practical continuous-variable quantum key distribution (CV-QKD) system, the optimization of modulation variance is crucial for promoting protocol performance. The optimization relies on algorithms like local search in general. But the efficiency of local search methods is limited in low latency and limited computing power scenarios due to their high computational consumption. Hence, this optimization approach is infeasible for satellite-based CV-QKD due to satellites’ low power feature. In this paper, a neural network model that directly predicting the optimal modulation variance in nearly real time is proposed for free space gaussian modulated CV-QKD protocols. This work enhances the feasibility of implementing CV-QKD in low-Earth-orbit satellite scenarios, where typical link durations of few seconds demand rapid parameter optimization. Moreover, a simulation platform for free space CV-QKD protocol, which employs the precise orbital model to extract the elevation angle and the transmission length, is designed and developed to generate training sets that make the neural network model more practical. Our work can be used as a foundation to support parameter prediction for future quantum communication satellite missions.

New Journal of Physics•Apr 1, 2025•3 citations

Optimal Parameter Prediction for Secure Quantum Key Distribution Using Quantum Machine Learning Models

B. Sathish Babu, K. Bhargavi, Kanakatti Subramanya

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.

IGI Global eBooks•Jan 1, 2021•0 citations

Final-XGBoost: a Bayesian-tuned machine learning model for quantum key distribution optimization

Qihao Xu, Cheng Wu, Fuqiang Wang +4 more

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.

Physica Scripta•Jun 2, 2025•1 citation

Optimal Parameter Prediction for Secure Quantum Key Distribution Using Quantum Machine Learning Models

B. Sathish Babu, K. Bhargavi, Kanakatti Subramanya

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.

Advances in information security, privacy, and ethics book series•Jan 1, 2020•5 citations

Real-time optimal protocol prediction of quantum key distribution using machine learning

R. Arthi, Nayana J.S., Rajarshee Mondal

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.

International Journal of Pervasive Computing and Communications•Aug 31, 2022•3 citations

Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution

Zhiping Liu, Min-Gang Zhou, Wen‐Bo Liu +4 more

Continuous-variable quantum key distribution (CV QKD) with discrete modulation has attracted increasing attention due to its experimental simplicity, lower-cost implementation and compatibility with classical optical communication. Correspondingly, some novel numerical methods have been proposed to analyze the security of these protocols against collective attacks, which promotes key rates over one hundred kilometers of fiber distance. However, numerical methods are limited by their calculation time and resource consumption, for which they cannot play more roles on mobile platforms in quantum networks. To improve this issue, a neural network model predicting key rates in nearly real time has been proposed previously. Here, we go further and show a neural network model combined with Bayesian optimization. This model automatically designs the best architecture of neural network computing key rates in real time. We demonstrate our model with two variants of CV QKD protocols with quaternary modulation. The results show high reliability with secure probability as high as 99.15% − 99.59%, considerable tightness and high efficiency with speedup of approximately 107 in both cases. This inspiring model enables the real-time computation of unstructured quantum key distribution protocols’ key rate more automatically and efficiently, which has met the growing needs of implementing QKD protocols on moving platforms.

arXiv:2201.09419•Optics Express•Apr 18, 2022•33 citations
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.
Abstract For a practical continuous-variable quantum key distribution (CV-QKD) system, the optimization of modulation variance is crucial for promoting protocol performance. The optimization relies on algorithms like local search in general. But the efficiency of local search methods is limited in low latency and limited computing power scenarios due to their high computational consumption. Hence, this optimization approach is infeasible for satellite-based CV-QKD due to satellites’ low power feature. In this paper, a neural network model that directly predicting the optimal modulation variance in nearly real time is proposed for free space gaussian modulated CV-QKD protocols. This work enhances the feasibility of implementing CV-QKD in low-Earth-orbit satellite scenarios, where typical link durations of few seconds demand rapid parameter optimization. Moreover, a simulation platform for free space CV-QKD protocol, which employs the precise orbital model to extract the elevation angle and the transmission length, is designed and developed to generate training sets that make the neural network model more practical. Our work can be used as a foundation to support parameter prediction for future quantum communication satellite missions.
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.
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 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.
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.
Continuous-variable quantum key distribution (CV QKD) with discrete modulation has attracted increasing attention due to its experimental simplicity, lower-cost implementation and compatibility with classical optical communication. Correspondingly, some novel numerical methods have been proposed to analyze the security of these protocols against collective attacks, which promotes key rates over one hundred kilometers of fiber distance. However, numerical methods are limited by their calculation time and resource consumption, for which they cannot play more roles on mobile platforms in quantum networks. To improve this issue, a neural network model predicting key rates in nearly real time has been proposed previously. Here, we go further and show a neural network model combined with Bayesian optimization. This model automatically designs the best architecture of neural network computing key rates in real time. We demonstrate our model with two variants of CV QKD protocols with quaternary modulation. The results show high reliability with secure probability as high as 99.15% − 99.59%, considerable tightness and high efficiency with speedup of approximately 107 in both cases. This inspiring model enables the real-time computation of unstructured quantum key distribution protocols’ key rate more automatically and efficiently, which has met the growing needs of implementing QKD protocols on moving platforms.