  Factorization Machine (FM) is the most commonly used model to build a
recommendation system since it can incorporate side information to improve
performance. However, producing item suggestions for a given user with a
trained FM is time-consuming. It requires a run-time of $O((N_m \log N_m)^2)$,
where $N_m$ is the number of items in the dataset. To address this problem, we
propose a quadratic unconstrained binary optimization (QUBO) scheme to combine
with FM and apply quantum annealing (QA) computation. Compared to classical
methods, this hybrid algorithm provides a faster than quadratic speedup in
finding good user suggestions. We then demonstrate the aforementioned
computational advantage on current NISQ hardware by experimenting with a real
example on a D-Wave annealer.
