Geometrical versus time-series representation of data in learning quantum control

We study the application of machine learning methods based on a geometrical and time-series character of data in the application to quantum control. We demonstrate that recurrent neural networks posses the ability to generalize the correction pulses with respect to the level of noise present in the system. We also show that the utilisation of the geometrical structure of control pulses is sufficient for achieving high-fidelity in quantum control using machine learning procedures.