The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods
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In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let N be a number of classifiers from an ensemble model and O(T) be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is O( N · T ). We propose an algorithm which works in O(√(N)· T).
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