Does Gradient Boosting detect non-linear relationships?

Question

I wish to train some data using the the Gradient Boosting Regressor of Scikit-Learn.

My questions are:

1) Is the algorithm able to capture non-linear relationships? For example, in the case of y=x^2, y increases as x approaches negative infinity and positive infinity. What if the graph looks like y=sin(x)?

2) Is the algorithm able to detect interactions/relationships among the features? Specifically, should I add features that are the sums/differences of the raw features to the training set?