Experimentally realizing efficient quantum control with reinforcement learning

作     者：Ming-Zhong Ai Yongcheng Ding Yue Ban JoséDMartín-Guerrero Jorge Casanova Jin-Ming Cui Yun-Feng Huang Xi Chen Chuan-Feng Li Guang-Can Guo Ming-Zhong Ai;Yongcheng Ding;Yue Ban;José D.Martín-Guerrero;Jorge Casanova;Jin-Ming Cui;Yun-Feng Huang;Xi Chen;Chuan-Feng Li;Guang-Can Guo

作者机构：CAS Key Laboratory of Quantum InformationUniversity of Science and Technology of ChinaHefei 230026China CAS Center For Excellence in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 230026China International Center of Quantum Artifcial Intelligence for Science and Technology(Qu Artist)and Department of PhysicsShanghai UniversityShanghai 200444China Department of Physical ChemistryUniversity of the Basque Country UPV/EHUBilbao 48080Spai School of Materials Science and EngineeringShanghai UniversityShanghai 200444China IDALElectronic Engineering DepartmentUniversity of ValenciaValencia 46100Spain IKERBASQUEBasque Foundation for ScienceBilbao 48009Spain 

出 版 物：《Science China(Physics,Mechanics & Astronomy)》 (中国科学：物理学、力学、天文学（英文版）)

年 卷 期：2022年第65卷第5期

页      面：13-20页

核心收录：

学科分类：0710[理学-生物学] 0711[理学-系统科学] 07[理学] 08[工学] 081104[工学-模式识别与智能系统] 070201[理学-理论物理] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.11874343,61327901,11774335,and 11734015) n-hui Initiative in Quantum Information Technologies(Grant Nos.AHY020100,and AHY070000) Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDYSSW-SLH003) supported by the National Natural Science Foundation of China(Grant No.12075145) STCSM(Grant No.2019SHZDZX01-ZX04) Program for Eastern Scholar,QMi CS(Grant No.820505) Open Super Q(Grant No.820363)of the EU Flagship on Quantum Technologies Spanish Government PGC2018-095113-B-I00(MCIU/AEI/FEDER,UE) Basque Government IT986-16 EU FET Open Grant Quromorphic(Grant No.828826) EPIQUS(Grant No.899368) the Ramony Cajal Program(Grant No.RYC-2017-22482) Ramony Cajal Program(Grant No.RYC2018-025197-I) the EUR2020-112117 Project of the Spanish MICINN the support from the UPV/EHU through the grant EHUr OPE。 

主　　题：quantum control reinforcement learning trapped ion quantum computing noise robustness 

摘      要：We experimentally investigate deep reinforcement learning(DRL)as an artificial intelligence approach to control a quantum system.We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity.In particular,the protocol’s robustness against both over-rotations and off-resonance errors can still be achieved simultaneously without any priori input.For the thorough comparison,we choose the task as single-qubit flipping,in which various analytical methods are well-developed as the benchmark,ensuring their feasibility in the quantum system as well.Consequently,a gate operation is demonstrated on a trapped^(171) Yb^(+)ion,significantly outperforming analytical pulses in the gate time and energy cost with hybrid robustness,as well as the fidelity after repetitive operations under time-varying stochastic errors.Our experiments reveal a framework of computer-inspired quantum control,which can be extended to other complicated tasks without loss of generality.