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, possess 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.<...

中文翻译：

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量子控制学习中数据的几何与时间序列表示

近来，机器学习技术已变得流行起来，用于分析物理系统并解决量子计算中出现的问题。在本文中，我们专注于使用此类技术来查找实现给定量子逻辑运算的物理运算的顺序。在这种情况下，我们分析了数据表示的灵活性，并根据数据的不同表示比较了两种机器学习方法的适用性。我们证明控制脉冲的几何结构的利用足以实现实现的演变的高保真度。我们还证明，与几何方法不同，人工神经网络具有泛化能力，使它们能够为干扰强度可变的系统生成控制脉冲。