Effective Hamiltonian methods are utilized to model the two-qubit
cross-resonance gate for both the ideal two-qubit case and when higher levels
are included. Analytic expressions are obtained in the qubit case and the
higher-level model is solved both perturbatively and numerically with the
solutions agreeing well in the weak drive limit. The methods are applied to
parameters from recent experiments and accounting for classical cross-talk
effects results in good agreement between theory and experimental results.

采用有效哈密顿方法对两个量子比特的交叉谐振门进行建模，并在理想的两个量子比特情况下和包括更高能级时进行研究。方法可用于最近实验的参数，包括经典串扰效应，结果表明理论与实验结果符合良好。

交叉共振门的有效哈密顿模型

Effective Hamiltonian models of the cross-resonance gate

Stacked generalization is a general method of using a high-level model to
combine lower-level models to achieve greater predictive accuracy. In this
paper we address two crucial issues which have been considered to be a `black
art' in classification tasks ever since the introduction of stacked
generalization in 1992 by Wolpert: the type of generalizer that is suitable to
derive the higher-level model, and the kind of attributes that should be used
as its input. We find that best results are obtained when the higher-level
model combines the confidence (and not just the predictions) of the lower-level
ones. We demonstrate the effectiveness of stacked generalization for combining
three different types of learning algorithms for classification tasks. We also
compare the performance of stacked generalization with majority vote and
published results of arcing and bagging.

本文研究如何使用高级模型组合低级模型以实现更高精度的预测，并发现高级模型结合更低级模型的置信度（而不仅仅是预测）可以获得最佳结果。我们还比较了堆叠泛化与多数表决以及 arcing 和 bagging 的性能表现。