Boosting Algorithms are considered as iterative functional gradient descent algorithms. These algorithms optimize a cost function over function space by iteratively choosing a function (weak hypothesis) that points in the negative gradient direction. Like other boosting methods, gradient boosting combines weak "learners" into a single strong learner in an iterative fashion.
In the quantum realm, it may be a good idea to invoke the Grover search algorithm to construct the gradient boosting algorithms in an unstructured database with the data structure of a binary tree or another suitable tree structure. Following sample code is an interesting customization of XGBoost in the classical setting. There is a reference implementation of Grover Search in Q# in the following repository. Instead of a single marked state as in this Q# example, we could construct a tree state as an input to implement Grover Search to run through a decision tree to realize quantum gradient tree boosting.