  Machine learning applications are limited by computational power. In this
paper, we gain novel insights into the application of quantum annealing (QA) to
machine learning (ML) through experiments in natural language processing (NLP),
seizure prediction, and linear separability testing. These experiments are
performed on QA simulators and early-stage commercial QA hardware and compared
to an unprecedented number of traditional ML techniques. We extend QBoost, an
early implementation of a binary classifier that utilizes a quantum annealer,
via resampling and ensembling of predicted probabilities to produce a more
robust class estimator. To determine the strengths and weaknesses of this
approach, resampled QBoost (RQBoost) is tested across several datasets and
compared to QBoost and traditional ML. We show and explain how QBoost in
combination with a commercial QA device are unable to perfectly separate binary
class data which is linearly separable via logistic regression with shrinkage.
We further explore the performance of RQBoost in the space of NLP and seizure
prediction and find QA-enabled ML using QBoost and RQBoost is outperformed by
traditional techniques. Additionally, we provide a detailed discussion of
algorithmic constraints and trade-offs imposed by the use of this QA hardware.
Through these experiments, we provide unique insights into the state of quantum
ML via boosting and the use of quantum annealing hardware that are valuable to
institutions interested in applying QA to problems in ML and beyond.
