The quantum version of random forest model for binary classification problem

Abstract

We suggest the quantum version of prediction using random forest model for binary classification problem. The idea of the paper is to combine quantum amplitude amplification algorithm and the probabilistic aggregation of the results of different decision trees in the forest. Quantum amplitude amplification algorithm is used as a subroutine and helps us to quadratically speed up a prediction. In the classical case, a random forest model works in, where is a number of trees in a forest and is a running time of prediction on one tree. The running time of our version is (√).