  Recent work has shown that quantum annealing for machine learning, referred
to as QAML, can perform comparably to state-of-the-art machine learning methods
with a specific application to Higgs boson classification. We propose QAML-Z, a
novel algorithm that iteratively zooms in on a region of the energy surface by
mapping the problem to a continuous space and sequentially applying quantum
annealing to an augmented set of weak classifiers. Results on a programmable
quantum annealer show that QAML-Z matches classical deep neural network
performance at small training set sizes and reduces the performance margin
between QAML and classical deep neural networks by almost 50% at large training
set sizes, as measured by area under the ROC curve. The significant improvement
of quantum annealing algorithms for machine learning and the use of a discrete
quantum algorithm on a continuous optimization problem both opens a new class
of problems that can be solved by quantum annealers and suggests the approach
in performance of near-term quantum machine learning towards classical
benchmarks.
