  Optimizing the training of a machine learning pipeline helps in reducing
training costs and improving model performance. One such optimizing strategy is
quantum annealing, which is an emerging computing paradigm that has shown
potential in optimizing the training of a machine learning model. The
implementation of a physical quantum annealer has been realized by D-Wave
systems and is available to the research community for experiments. Recent
experimental results on a variety of machine learning applications using
quantum annealing have shown interesting results where the performance of
classical machine learning techniques is limited by limited training data and
high dimensional features. This article explores the application of D-Wave's
quantum annealer for optimizing machine learning pipelines for real-world
classification problems. We review the application domains on which a physical
quantum annealer has been used to train machine learning classifiers. We
discuss and analyze the experiments performed on the D-Wave quantum annealer
for applications such as image recognition, remote sensing imagery,
computational biology, and particle physics. We discuss the possible advantages
and the problems for which quantum annealing is likely to be advantageous over
classical computation.
