信息工程大学学报2025，Vol.26Issue(4)：462-469,8.DOI:10.3969/j.issn.1671-0673.XXXX.XX.001
分布式强化学习驱动的量子编译自动调优方法
A Distributed Reinforcement Learning Driven Approach to Automatic Tuning of Quantum Compilation
刘毅 1朱雨 1许瑾晨 1杜启明 1连航 1涂政1
作者信息
摘要
Abstract
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%.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Key words
reinforcement learning/distributed system/deep Q-network/quantum compilation opti-mization/auto-tuning quantum compilation分类
信息技术与安全科学引用本文复制引用
刘毅,朱雨,许瑾晨,杜启明,连航,涂政..分布式强化学习驱动的量子编译自动调优方法[J].信息工程大学学报,2025,26(4):462-469,8.
