  Quantum machine learning has the potential to enable advances in artificial
intelligence, such as solving problems intractable on classical computers. Some
fundamental ideas behind quantum machine learning are similar to kernel methods
in classical machine learning. Both process information by mapping it into
high-dimensional vector spaces without explicitly calculating their numerical
values. We explore a setup for performing classification on labeled classical
datasets, consisting of a classical neural network connected to a quantum
annealer. The neural network programs the quantum annealer's controls and
thereby maps the annealer's initial states into new states in the Hilbert
space. The neural network's parameters are optimized to maximize the distance
of states corresponding to inputs from different classes and minimize the
distance between quantum states corresponding to the same class. Recent
literature showed that at least some of the "learning" is due to the quantum
annealer, connecting a small linear network to a quantum annealer and using it
to learn small and linearly inseparable datasets. In this study, we consider a
similar but not quite the same case, where a classical fully-fledged neural
network is connected with a small quantum annealer. In such a setting, the
fully-fledged classical neural-network already has built-in nonlinearity and
learning power, and can already handle the classification problem alone, we
want to see whether an additional quantum layer could boost its performance. We
simulate this system to learn several common datasets, including those for
image and sound recognition. We conclude that adding a small quantum annealer
does not provide a significant benefit over just using a regular (nonlinear)
classical neural network.
