  Quantum machine learning seeks to exploit the underlying nature of a quantum
computer to enhance machine learning techniques. A particular framework uses
the quantum property of superposition to store sets of parameters, thereby
creating an ensemble of quantum classifiers that may be computed in parallel.
The idea stems from classical ensemble methods where one attempts to build a
stronger model by averaging the results from many different models. In this
work, we demonstrate that a specific implementation of the quantum ensemble of
quantum classifiers, called the accuracy-weighted quantum ensemble, can be
fully dequantised. On the other hand, the general quantum ensemble framework is
shown to contain the well-known Deutsch-Jozsa algorithm that notably provides a
quantum speedup and creates the potential for a useful quantum ensemble to
harness this computational advantage.
