TY - JOUR
T1 - Learning-Based Quantum Robust Control
T2 - Algorithm, Applications, and Experiments
AU - Dong, Daoyi
AU - Xing, Xi
AU - Ma, Hailan
AU - Chen, Chunlin
AU - Liu, Zhixin
AU - Rabitz, Herschel
N1 - Funding Information:
Manuscript received November 1, 2018; revised April 20, 2019; accepted June 1, 2019. Date of publication July 10, 2019; date of current version July 10, 2020. This work was supported in part by the Australian Research Council’s Discovery Projects under Grant DP190101566, in part by the National Natural Science Foundation of China under Grant 61828303 and Grant 61833010, in part by NSF under Grant CHE-1464569, and in part by Army Research Office under Grant W911NF-16-1-0014. This paper was recommended by Associate Editor L. Cheng. (Corresponding author: Daoyi Dong.) D. Dong is with the School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia, and also with the Department of Chemistry, Princeton University, Princeton, NJ 08544 USA (e-mail: daoyidong@gmail.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.
AB - Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.
KW - Differential evolution
KW - femtosecond laser
KW - quantum control
KW - quantum learning
KW - quantum robust control
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U2 - 10.1109/TCYB.2019.2921424
DO - 10.1109/TCYB.2019.2921424
M3 - Article
C2 - 31295133
AN - SCOPUS:85076736075
SN - 2168-2267
VL - 50
SP - 3581
EP - 3593
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 8
M1 - 8759071
ER -