受驱动二能级量子系统的监督学习表征
Characterization of a driven two-level quantum system by Supervised Learning
摘要：
我们研究了在何种程度上受到外部时间相关驱动的两能级量子系统可以通过监督学习来表征。我们应用这种方法的情况下，bang-bang控制和估计的偏移量和最终的距离到一个给定的目标状态。对于任何控制协议，目标是找到偏移和距离之间的映射。该映射使用神经网络进行插值。估计是全球性的意义上说，没有先验知识是需要确定的关系。在一系列数据集上测试了不同的神经网络算法。我们表明，映射可以再现非常高的精度在直接的情况下，当偏移量是已知的，而障碍物出现在间接的情况下，从距离到目标。我们指出的估计过程中的限制相对于属性的映射插值。我们讨论了不同结果的物理相关性。
英文摘要：
We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. For any control protocol, the goal is to find the mapping between the offset and the distance. This mapping is interpolated using a neural network. The estimate is global in the sense that no a priori knowledge is required on the relation to be determined. Different neural network algorithms are tested on a series of data sets. We show that the mapping can be reproduced with very high precision in the direct case when the offset is known, while obstacles appear in the indirect case starting from the distance to the target. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.
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