Intelligent recommender system based on quantum clustering and matrix completion

Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time-consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource-consuming step, especially when working with large datasets. In this article, we propose a new better-performing recommender system that operates as a combination of an adapted quantum K-means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K-means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.

Automated summary

Quantum algorithms, benefiting from the superposition property of quantum information, offer significant speedup compared to classical algorithms. In this article, a new recommender system combining an adapted quantum K-means algorithm and the singular value decomposition algorithm is proposed, achieving better performance than previous systems tested.