Recommendation systems with quantum k-NN and Grover's algorithms for data processing

Abstract

In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows a main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps during recommendation system was also indicated. The verification correctness of a proposed recommendation system was also analysed, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.

arXiv comment17 pages, 5 figures

References in corpus (11)

• Elementary gates for quantum computation
• Improved Simulation of Stabilizer Circuits
• Quantum Computing
• Experimental One-Way Quantum Computing
• Information and Computation: Classical and Quantum Aspects
• An Exact Quantum Polynomial-Time Algorithm for Simon's Problem
• Grover's Quantum Search Algorithm for an Arbitrary Initial Amplitude Distribution
• A Family of Grover's Quantum Searching Algorithms
• Pattern recognition on the quantum Bloch sphere
• Dynamic Grover Search: Applications in Recommendation systems and Optimization problems
• Generalised quantum weakest preconditions

Cited by in corpus (2)

• Feature Selection for Recommender Systems with Quantum Computing
• Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification