Abstract
Robust control of open quantum systems from one state to another is much more difficult than closed quantum systems as a result of system-environment interactions. In this paper, we adopt the sampling-based learning control approach with the motivation of utilizing some artificial samples instead of unknown uncertainties to design an optimal control field against parameter fluctuations. To enhance the learning performance, we introduce an improved differential evolution (DE) algorithm with equally-mixed strategies in the training step of the control design for open quantum systems. Numerical results verify the effectiveness of the proposed equally-mixed strategies DE (EMSDE) algorithm regarding the control design for open quantum systems with uncertainties.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
