The quantum version of prediction for binary classification problem by ensemble methods

In this work, we consider the performance of using a quantum algorithm to predict the result of a binary classification problem when a machine learning model is an ensemble of any simple classifiers. This approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let <i>N</i> be the number of classifiers from an ensemble model and <i>O</i>(<i>T</i>) be the running time of prediction of one classifier. In classical case, the final result is obtained by ”averaging” outcomes of all ensemble model’s classifiers. The running time in classical case is <i>O</i> (<i>N</i> · <i>T</i>). We propose an algorithm that works in <br/> <i>O</i> (√<i>N</i> · <i>T</i> ).

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