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...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.展开更多
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)+12 种基金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 TechnologiesSpanish Government PGC2018-095113-B-I00(MCIU/AEI/FEDER,UE)Basque Government IT986-16EU 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 MICINNthe support from the UPV/EHU through the grant EHUr OPE。
文摘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.
