  The promise of quantum computing to open new unexplored possibilities in
several scientific fields has been long discussed, but until recently the lack
of a functional quantum computer has confined this discussion mostly to
theoretical algorithmic papers. It was only in the last few years that small
but functional quantum computers have become available to the broader research
community. One paradigm in particular, quantum annealing, can be used to sample
optimal solutions for a number of NP-hard optimization problems represented
with classical operations research tools, providing an easy access to the
potential of this emerging technology. One of the tasks that most naturally
fits in this mathematical formulation is feature selection. In this paper, we
investigate how to design a hybrid feature selection algorithm for recommender
systems that leverages the domain knowledge and behavior hidden in the user
interactions data. We represent the feature selection as an optimization
problem and solve it on a real quantum computer, provided by D-Wave. The
results indicate that the proposed approach is effective in selecting a limited
set of important features and that quantum computers are becoming powerful
enough to enter the wider realm of applied science.
