Abstract
Quantum control landscape is generally a physical objective defined as a functional of the control field and plays an important role for the analysis and manipulation of quantum systems. In this paper, we focus on typical learning methods (i.e., gradient decent method, genetic algorithm and deferential evolution) for the landscape control of open quantum systems and explores the characteristics of these different types of learning algorithms. Taking a two-level open quantum system as an example, the optimal value of the control landscape can be obtained under varying Lindblad operators that reflect the system's interactions with the environment. Numerical experiments demonstrate the learning performances to acquire the optimal control strategy by exploring the control landscape of this two-level open quantum system.
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