Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer
This addresses runtime inefficiencies in recommendation systems for users needing faster suggestions, though it appears incremental as it adapts existing quantum annealing to a known bottleneck.
The authors tackled the computational bottleneck of producing recommendations with trained Factorization Machines by formulating it as a QUBO problem and applying quantum annealing, achieving faster-than-quadratic speedup compared to classical methods on current NISQ hardware.
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.