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Recently machine learning techniques have become popular for analysingphysical systems and solving problems occurring in quantum computing. In thispaper we focus on using such techniques for finding the sequence of physicaloperations implementing the given quantum logical operation. In this context weanalyse the flexibility of the data representation and compare theapplicability of two machine learning approaches based on differentrepresentations of data. We demonstrate that the utilization of the geometricalstructure of control pulses is sufficient for achieving high-fidelity of theimplemented evolution. We also demonstrate that artificial neural networks,unlike geometrical methods, posses the generalization abilities enabling themto generate control pulses for the systems with variable strength of thedisturbance. The presented results suggest that in some quantum controlscenarios, geometrical data representation and processing is competitive tomore complex methods.

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