摘要
针对强化学习模型应用到量子编译自动调优领域开销大的问题,提出一种分布式强化学习(DRL)驱动的量子编译自动调优方法,通过将经验生成与智能体训练解耦,基于分布式集群实现了并行经验生成。该方法通过建立具有稠密奖励特性的量子编译马尔可夫决策过程(MDP)模型,设计经验生成与智能体训练的解耦机制,结合动态经验加载策略,在保证优化效果的同时提升训练效率。实验结果表明,分布式训练框架训练耗时减少54.6%;优化性能方面,智能体在测试集77.3%的量子线路上表现优于Qiskit-O3编译器,对未见过的Shor算法线路平均减少17.4%量子门数量。
Aiming at the problem of high overhead when applying reinforcement learning models to the field of automatic tuning of quantum compilation,a distributed reinforcement learning(DRL)driven au-tomatic tuning method for quantum compilation is proposed.By decoupling experience generation from agent training,parallel experience generation is achieved based on a distributed cluster.In this method,a markov decision process(MDP)model of quantum compilation is established with the char-acteristic of dense rewards,a decoupling mechanism for experience generation and agent trainingis is designed,and a dynamic experience loading strategy is combined to improve the training efficiency while ensuring the optimization effect.Experiments demonstrate a 54.6%reduction in training time compared to baseline methods.In terms of optimization performance,the agent performs better than the Qiskit-O3 compiler on 77.3%of the quantum circuits in the test set,and the number of quantum gates of the unseen Shor algorithm circuits is reduced by an average of 17.4%.
作者
刘毅
朱雨
许瑾晨
杜启明
连航
涂政
LIU Yi;ZHU Yu;XU Jinchen;DU Qiming;LIAN Hang;TU Zheng(Information Engineering University,Zhengzhou 450001,China)
出处
《信息工程大学学报》
2025年第4期462-469,共8页
Journal of Information Engineering University
关键词
强化学习
分布式系统
深度Q网络
量子编译优化
量子编译自动调优
reinforcement learning
distributed system
deep Q-network
quantum compilation opti-mization
auto-tuning quantum compilation
