Quafu-RL:The cloud quantum computers based quantum reinforcement learning  

作　　者：靳羽欣 许宏泽 王正安 庄伟峰 黄凯旋 时运豪 马卫国 李天铭 陈驰通 许凯 冯玉龙 刘培 陈墨 李尚书 杨智鹏 钱辰 马运恒 肖骁 钱鹏 顾炎武 柴绪丹 普亚南 张翼鹏 魏世杰 曾进峰 李行 龙桂鲁 金贻荣 于海峰 范桁 刘东 胡孟军 Yu-Xin Jin;Hong-Ze Xu;Zheng-An Wang;Wei-Feng Zhuang;Kai-Xuan Huang;Yun-Hao Shi;Wei-Guo Ma;Tian-Ming Li;Chi-Tong Chen;Kai Xu;Yu-Long Feng;Pei Liu;Mo Chen;Shang-Shu Li;Zhi-Peng Yang;Chen Qian;Yun-Heng Ma;Xiao Xiao;Peng Qian;Yanwu Gu;Xu-Dan Chai;Ya-Nan Pu;Yi-Peng Zhang;Shi-Jie Wei;Jin-Feng Zeng;Hang Li;Gui-Lu Long;Yirong Jin;Haifeng Yu;Heng Fan;Dong E.Liu;Meng-Jun Hu(Beijing Academy of Quantum Information Sciences,Beijing 100193,China;State Key Laboratory of Low Dimensional Quantum Physics,Department of Physics,Tsinghua University,Beijing 100084,China;Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China;Frontier Science Center for Quantum Information,Beijing 100184,China;School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China;CAS Center for Excellence in Topological Quantum Computation,University of Chinese Academy of Sciences,Beijing 100190,China;School of Mathematical Sciences,Nankai University,Tianjin 300071,China)

机构地区：[1]Beijing Academy of Quantum Information Sciences,Beijing 100193,China [2]State Key Laboratory of Low Dimensional Quantum Physics,Department of Physics,Tsinghua University,Beijing 100084,China [3]Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China [4]Frontier Science Center for Quantum Information,Beijing 100184,China [5]School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China [6]CAS Center for Excellence in Topological Quantum Computation,University of Chinese Academy of Sciences,Beijing 100190,China [7]School of Mathematical Sciences,Nankai University,Tianjin 300071,China

出　　处：《Chinese Physics B》2024年第5期29-34,共6页中国物理B（英文版）

基　　金：supported by the Beijing Academy of Quantum Information Sciences;supported by the National Natural Science Foundation of China(Grant No.92365206);the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272);supported by the National Natural Science Foundation of China(Grant No.12247168);China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。

摘　　要：With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

关 键 词：quantum cloud platform quantum reinforcement learning evolutionary quantum architecture search 

分 类 号：O413[理学—理论物理] TP38[理学—物理] TP18[自动化与计算机技术—计算机系统结构]