Optimizing quantum control pulses with complex constraints and few variables through autodifferentiation

Song, Yao1; Li, Junning2; Hai, Yong-Ju1,3; Guo, Qihao1; Deng, Xiu-Hao1,4

2022-01-31

10.1103/PhysRevA.105.012616

PHYSICAL REVIEW A   影响因子和分区

2469-9926

2469-9934

Applying optimal control algorithms on realistic quantum systems confronts two key challenges: to efficiently adopt physical constraints in the optimization and to minimize the variables for the convenience of experimental tuneups. To resolve these issues, we propose an algorithm by incorporating multiple constraints into the gradient optimization over piecewise pulse constant values, which are transformed to contained numbers of the finite Fourier basis for bandwidth control. Such complex constraints and variable transformation involved in the optimization introduce extreme difficulty in calculating gradients. We resolve this issue efficiently utilizing autodifferentiation on the machine learning platform TensorFlow. We test our algorithm by finding smooth control pulses to implement single-qubit and two-qubit gates for superconducting transmon qubits with alwayson interaction, which remains a challenge of quantum control in various qubit systems. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints.

SCI ; EI

英语

第一 ; 通讯

Key-Area Research and Development Program of Guang-Dong Province[2018B030326001] ; National Natural Science Foundation of China[U1801661] ; Guangdong Innovative and Entrepreneurial Research Team Program[2016ZT06D348] ; Guangdong Provincial Key Laboratory[2019B121203002] ; Natural Science Foundation of Guang-dong Province[2017B030308003] ; Science, Technol-ogy, and Innovation Commission of Shenzhen Municipality["JCYJ20170412152620376","KYTDPT20181011104202253"] ; NSF of Beijing[Z190012] ; Shenzhen Science and Technology Program[KQTD20200820113010023]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000751856100019

AMER PHYSICAL SOC

20220611608313

Control theory ; Qubits

Control Systems:731.1 ; Light, Optics and Optical Devices:741 ; Light/Optics:741.1 ; Nanotechnology:761 ; Quantum Theory; Quantum Mechanics:931.4

PHYSICS

Web of Science

1.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China
2.City Univ Hong Kong, Dept Phys, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China
3.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Guangdong Prov Key Lab Quantum Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China

Song, Yao,Li, Junning,Hai, Yong-Ju,et al. Optimizing quantum control pulses with complex constraints and few variables through autodifferentiation[J]. PHYSICAL REVIEW A,2022,105(1).

Song, Yao,Li, Junning,Hai, Yong-Ju,Guo, Qihao,&Deng, Xiu-Hao.(2022).Optimizing quantum control pulses with complex constraints and few variables through autodifferentiation.PHYSICAL REVIEW A,105(1).

Song, Yao,et al."Optimizing quantum control pulses with complex constraints and few variables through autodifferentiation".PHYSICAL REVIEW A 105.1(2022).