论文标题
通过监督学习来表征驱动的两级量子系统
Characterization of a driven two-level quantum system by Supervised Learning
论文作者
论文摘要
我们研究了受到监督的学习的特征，可以在多大程度上进行遭受外部时间依赖驱动器的两级量子系统的特征。我们将此方法应用于Bang-Bang控制的情况以及对给定目标状态的偏移和最终距离的估计。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插值。估计是全球性的，从某种意义上说，在要确定的关系上不需要先验知识。在一系列数据集上测试了不同的神经网络算法。我们表明，在已知偏移时，可以在直接情况下以很高的精度复制映射，而障碍物则从距离从距离到目标开始。我们指出了估计过程的限制，相对于要插值的映射的属性。我们讨论了不同结果的身体相关性。
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.
