论文标题
实施训练的分解机器推荐系统对量子退火器的推荐系统
Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer
论文作者
论文摘要
分解机（FM）是建立推荐系统的最常用模型，因为它可以合并侧面信息以提高性能。但是，为具有训练有素的FM的给定用户制作项目建议很耗时。它需要$ o的运行时间（（n_m \ log n_m）^2）$，其中$ n_m $是数据集中的项目数。为了解决这个问题，我们提出了一个二次无约束的二进制优化（QUBO）方案，以与FM结合并应用量子退火（QA）计算。与经典方法相比，该混合算法在寻找好的用户建议时提供的速度比二次加速更快。然后，我们通过在D-Wave退火器上尝试一个真实示例来证明当前NISQ硬件上的上述计算优势。
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.
