Learning Control of Quantum Systems Using Frequency-Domain Optimization Algorithms

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.

Automated summary

This study investigates two classes of quantum control problems in the context of ultrafast laser control of quantum systems using frequency-domain optimization algorithms. In the first class, a known system model is utilized with a gradient-based optimization algorithm to find an optimal control field. In the second class, an experimental system with an unknown model is considered, and a mixed strategy differential evolution algorithm is used to search for optimal control fields.