@inproceedings{747ff43ce3484a7e8cdc2418d9fe3b02,
title = "Robust entanglement control between two atoms in a cavity using sampling-based learning control",
abstract = "In this paper, a sampling-based learning control (SLC) algorithm is used to find a robust control law that can steer a quantum system with uncertainties into a maximally entangled state. The quantum system under consideration consists of two two-level atoms interacting with a quantized electromagnetic field. In the sampling-based learning control method, an artificial system is constructed based on the quantum system with uncertainties and an optimal control law is learned for the artificial system. Some additional samples which are generated by sampling the uncertainty parameters are used to test the performance of the optimal control law. Numerical results demonstrate the effectiveness of the SLC method in finding a robust control law for entanglement generation between two atoms in a cavity in the presence of a quantized field.",
keywords = "maximum entanglement, quantum control, sampling-based learning control, uncertainties",
author = "Mabrok, {Mohamed A.} and Daoyi Dong and Chunlin Chen and Petersen, {Ian R.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 ; Conference date: 15-12-2014 Through 17-12-2014",
year = "2014",
doi = "10.1109/CDC.2014.7040297",
language = "English",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "February",
pages = "5802--5807",
booktitle = "53rd IEEE Conference on Decision and Control,CDC 2014",
address = "United States",
edition = "February",
}