  Building on the quantum ensemble based classifier algorithm of Schuld and
Petruccione [arXiv:1704.02146v1], we devise equivalent classical algorithms
which show that this quantum ensemble method does not have advantage over
classical algorithms. Essentially, we simplify their algorithm until it is
intuitive to come up with an equivalent classical version. One of the classical
algorithms is extremely simple and runs in constant time for each input to be
classified. We further develop the idea and, as the main contribution of the
paper, we propose methods inspired by combining the quantum ensemble method
with adaptive boosting. The algorithms were tested and found to be comparable
to the AdaBoost algorithm on publicly available data sets.
