Quantum key distribution parameter optimization method based on random forest algorithm

Abstract

The invention relates to the field of quantum communication, and provides a quantum key distribution parameter optimization method based on a random forest algorithm. Under the condition of limited data length, globally optimized parameters can significantly improve security code rate of an actual decoy state QKD system. A traditional local search algorithm can be used for searching optimal parameters, but generally consumes more time resources and computing resources, and cannot meet the requirements of real-time parameter optimization of a high-speed QKD system and optimal configuration of large quantum communication network resources. According to the method, the random forest model is trained in advance by utilizing the original data, and then the optimal parameter is directly predicted by utilizing the random forest model, so that the to-be-optimized transmission parameter can be rapidly and accurately predicted according to the current system configuration, and the parameter optimization process is greatly accelerated. Numerical simulation proves that compared with a traditional search algorithm, the method is lower in time cost, higher in prediction precision and good in application prospect in a high-speed QKD system and a future large-scale quantum communication network.

Description

technical field [0001] The invention relates to the technical field of quantum communication, in particular to parameter optimization of quantum key distribution. Background technique [0002] For the decoy state Quantum Key Distribution (QKD, Quantum Key Distribution) system with limited data length, fully optimized parameters can greatly improve its key rate. In recent years, techniques to implement QKD on mobile platforms such as drones, handheld systems, and even satellites have attracted much attention, which requires parameter optimization to be performed with low latency and very limited computing power. In addition, with the development of the Internet of Things, a promising development direction of QKD may be the quantum network with multi-device interconnection, which poses a huge computational challenge to the controller for large-scale network parameter optimization. Traditionally, such parameter optimization relies on exhaustive search algorithms or local searc...

AI Technical Summary

Problems solved by technology

In addition, with the development of the Internet of Things, a promising development direction of QKD may be the quantum network with multi-device interconnection, which poses a huge computational challenge to the controller for large-scale network parameter optimization.

Traditionally, such parameter optimization relies on exhaustive search algorithms or local search algorithms. These algorithms are computationally intensive and run slowly on low-power platforms, which will inevitably lead to system delays and cannot meet the needs of large-scale QKD systems and QKD networks. , which is infeasible even for moderately sized networks

Images