基于强化学习的量子密钥资源分配数据集

基于强化学习的量子密钥资源分配数据集主要面向物联网环境下基于量子密钥分发机制和体系研究，物联网对轻量级和系统效率需求建设，基于南京网络通信与安全紫金山实验室PC主机上的仿真实验产生，主要记录了训练量子密钥请求的信息，其中包括量子密钥请求状态、请求到达时间、量子密钥量和安全性需求这四个数据项，由此可以基于强化学习方法来训练出有效的量子密钥资源分配方法，数据量156KB。This dataset for reinforcement learning-based quantum key resource allocation is primarily targeted at research on quantum key distribution (QKD) mechanisms and architectures in Internet of Things (IoT) environments, as well as the development of IoT systems that meet the demands for lightweight deployment and high system efficiency. It was generated via simulation experiments conducted on a PC host at the Purple Mountain Laboratories for Network and Communication Security in Nanjing. The dataset mainly records training-related data of quantum key requests, including four data items: quantum key request status, request arrival time, quantum key quantity, and security requirements. Effective quantum key resource allocation methods can be trained using reinforcement learning approaches based on this dataset, with a total data size of 156 KB.

搜集汇总

背景与挑战

背景概述

该数据集面向物联网环境，基于量子密钥分发机制，通过仿真实验生成量子密钥请求信息，包括状态、到达时间、密钥量和安全性需求等数据项。它旨在利用强化学习方法训练有效的量子密钥资源分配策略，数据量约为156KB。