A Survey on Quantum Computing for Recommendation Systems

Recommendation systems play a key role in everyday life; they are used to suggest items that are selected among many candidates that usually belong to huge datasets. The recommendations require a good performance both in terms of speed and the effectiveness of the provided suggestions. At the same time, one of the most challenging approaches in computer science is quantum computing. This computational paradigm can provide significant acceleration for resource-demanding and time-consuming algorithms. It has become very popular in recent years, thanks to the different tools available to the scientific and technical communities. Since performance has great relevance in recommendation systems, many researchers in the scientific community have recently proposed different improvements that exploit quantum approaches to provide better performance in recommendation systems. This paper gives an overview of the current state of the art in the literature, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain.