Geometrical versus time-series representation of data in quantum control learning
This work addresses quantum control optimization for researchers, but it appears incremental as it compares existing representations without introducing a new paradigm.
The paper tackled the problem of finding physical operation sequences for quantum logical operations by comparing geometrical and time-series data representations in machine learning, demonstrating that geometrical methods achieve high-fidelity evolution and neural networks offer generalization for variable disturbances.
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.