Application of quantum k-NN and grover’s algorithms for recommendation big-data system

Abstract

The growing size of modern databases and recommendation systems make it necessary to use a more efficient hardware and also software solutions that will meet the requirements of users of such systems. These requirements apply to both the size of databases and the speed of response, quality of recommendation. The evolving techniques of the quantum computational model offer a new computing possibilities. This chapter presents an approach based on the quantum algorithm of k-nearest neighbours, and Grover’s algorithm for building a recommendation system. The algorithmic correctness of the proposed system is analysed. The advantages of the presented solution are also indicated such as exponential capacity system and response speed which are independent of the amount of classic data stored in the quantum system. The final computational complexity does not depend on the amount of features but only on the length of the feature.