检索结果-南通市图书馆

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

Chinese Physics B 2024年第5期33卷 29-34页

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 Beijing Academy of quantum Information Sciences Beijing 100193China State Key Laboratory of Low Dimensional quantum Physics Department of PhysicsTsinghua UniversityBeijing 100084China Institute of Physics Chinese Academy of SciencesBeijing 100190China Frontier Science Center for quantum Information Beijing 100184China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Topological quantum Computation University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences Nankai UniversityTianjin 300071China

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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Complexity Analysis of quantum reinforcement learning

Complexity Analysis of Quantum Reinforcement Learning

引用

第二十九届中国控制会议

作者： CHEN Chunlin1,DONG Daoyi2 1.Department of Control and System Engineering,Nanjing University,Nanjing 210093,P.R.China2.School of Engineering and Information Technology,University of New South Wales at the Australian Defence Force Academy,Canberra,ACT 2600,Australia

quantum reinforcement learning has been systematically presented in a recent paper[Dong et al,quantum reinforcement learning,IEEE Transactions on Systems,Man and Cybernetics,Part B:Cybernetics,Vol.38,No.5,pp.1207-1220,2008],and such results have shown that quantum reinforcement learning is an effective approach for the solutions to some complex problems.The purpose of this paper is to analyze the complexity of quantum reinforcement learning.In particular,storage complexity and exploration complexity are defined and a collection of results are presented to demonstrate such complexities by several simple examples.

关键词： reinforcement learning quantum reinforcement learning Storage complexity Exploration complexity

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：