Learning Control of Quantum Systems Using Frequency-Domain Optimization Algorithms

被引：14

作者：

Dong, Daoyi ^{[1
,2
]}

Shu, Chuan-Cun ^{[1
,3
]}

Chen, Jiangchao ^{[2
]}

Xing, Xi ^{[2
]}

Ma, Hailan ^{[1
]}

Guo, Yu ^{[4
]}

Rabitz, Herschel ^{[2
]}

机构：

[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia

[2] Princeton Univ, Dept Chem, Princeton, NJ 08544 USA

[3] Cent South Univ, Sch Phys & Elect, Hunan Key Lab Super Microstruct & Ultrafast Proc, Changsha 410083, Peoples R China

[4] Changsha Univ Sci & Technol, Sch Phys & Elect Sci, Hunan Prov Key Lab Flexible Elect Mat Genome Engn, Changsha 410114, Peoples R China

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2021年 / 29卷 / 04期

基金：

中国国家自然科学基金; 澳大利亚研究理事会;

关键词：

Frequency-domain analysis; Optimization; Optimal control; Laser modes; Robustness; Atomic beams; Femtosecond laser; frequency-domain optimization; learning control; quantum control; quantum control experiment; DIFFERENTIAL EVOLUTION; PRECURSORS; DESIGN; PHOTOFRAGMENTATION; DYNAMICS; MOCVD;

D O I：

10.1109/TCST.2020.3018500

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We investigate two classes of quantum control problems by using frequency-domain optimization algorithms in the context of ultrafast laser control of quantum systems. In the first class of problems, the system model is known and a frequency-domain gradient-based optimization algorithm is applied for searching an optimal control field to selectively and robustly manipulate the population transfer in atomic rubidium. The other class of quantum control problems involves an experimental system with an unknown model. In this case, we introduce a differential evolution algorithm with a mixed strategy to search for optimal control fields and demonstrate the capability in an ultrafast laser control experiment for the fragmentation of Pr(hfac)(3) molecules.

引用

页码：1791 / 1798

页数：8

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