  Recently machine learning techniques have become popular for analysing
physical systems and solving problems occurring in quantum computing. In this
paper we focus on using such techniques for finding the sequence of physical
operations implementing the given quantum logical operation. In this context we
analyse the flexibility of the data representation and compare the
applicability of two machine learning approaches based on different
representations of data. We demonstrate that the utilization of the geometrical
structure of control pulses is sufficient for achieving high-fidelity of the
implemented evolution. We also demonstrate that artificial neural networks,
unlike geometrical methods, posses the generalization abilities enabling them
to generate control pulses for the systems with variable strength of the
disturbance. The presented results suggest that in some quantum control
scenarios, geometrical data representation and processing is competitive to
more complex methods.
