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.
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Acknowledgments
We would like to thank for useful discussions with the Q-INFO group at the Institute of Control and Computation Engineering (ISSI) of the University of Zielona Góra, Poland. We would like also to thank to anonymous referees for useful comments on the preliminary version of this chapter. The numerical results were done using the hardware and software available at the “GPU \(\mu \)-Lab” located at the Institute of Control and Computation Engineering of the University of Zielona Góra, Poland.
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Sawerwain, M., Wróblewski, M. (2019). Application of Quantum k-NN and Grover’s Algorithms for Recommendation Big-Data System. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. ISAT 2018. Advances in Intelligent Systems and Computing, vol 852. Springer, Cham. https://doi.org/10.1007/978-3-319-99981-4_22
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DOI: https://doi.org/10.1007/978-3-319-99981-4_22
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