An improved differential evolution algorithm for learning high-fidelity quantum controls

Yang, Xiaodong1,2; Li, Jun3,4,5,6; Peng, Xinhua1,2,7

2019-10-15

10.1016/j.scib.2019.07.013

Science Bulletin   影响因子和分区

2095-9273

2095-9281

Precisely and efficiently designing control pulses for the preparation of quantum states and quantum gates are the fundamental tasks for quantum computation. Gradient-based optimal control methods are the routine to design such pulses. However, the gradient information is often difficult to calculate or measure, especially when the system is not well calibrated or in the presence of various uncertainties. Gradient-free evolutionary algorithm is an alternative choice to accomplish this task but usually with low-efficiency. Here, we design an efficient mutation rule by using the information of the current and the former individuals together. This leads to our improved differential evolution algorithm, called daDE. To demonstrate its performance, we numerically benchmark the pulse optimization for quantum states and quantum gates preparations on small-scale NMR system. Further numerical comparisons with conventional differential evolution algorithms show that daDE has great advantages on the convergence speed and robustness to several uncertainties including pulse imperfections and measurement errors. (C) 2019 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.

Control pulses searching Differential evolution Quantum states and gates preparation Pulse imperfections Random measurement errors

SCI ; EI

英语

通讯

Anhui Initiative in Quantum Information Technologies[AHY050000]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:000487228100008

ELSEVIER

20193207280510

Benchmarking ; Evolutionary algorithms ; Logic gates ; Measurement errors ; Optimization ; Quantum channel ; Random errors ; Uncertainty analysis

Logic Elements:721.2 ; Optimization Techniques:921.5 ; Probability Theory:922.1 ; Quantum Theory; Quantum Mechanics:931.4

Web of Science

1.Univ Sci & Technol China, CAS Key Lab Microscale Magnet Resonance, Hefei 230026, Anhui, Peoples R China
2.Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Anhui, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
5.Ctr Quantum Comp, Peng Cheng Lab, Shenzhen 518055, Peoples R China
6.Southern Univ Sci & Technol, Shenzhen Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China
7.Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China

Yang, Xiaodong,Li, Jun,Peng, Xinhua. An improved differential evolution algorithm for learning high-fidelity quantum controls[J]. Science Bulletin,2019,64(19):1402-1408.

Yang, Xiaodong,Li, Jun,&Peng, Xinhua.(2019).An improved differential evolution algorithm for learning high-fidelity quantum controls.Science Bulletin,64(19),1402-1408.

Yang, Xiaodong,et al."An improved differential evolution algorithm for learning high-fidelity quantum controls".Science Bulletin 64.19(2019):1402-1408.