The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods

• URL: http://arxiv.org/abs/2112.13346v1
• Date: Sun, 26 Dec 2021 10:25:34 GMT
• Title: The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods
• Authors: Kamil Khadiev and Liliia Safina
• Abstract summary: We consider the performance of using a quantum algorithm to predict a result for a binary classification problem. We propose an algorithm which works in $Oleft(sqrtN cdot Tright)$.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $O\left( N \cdot T \right)$. We propose an algorithm which works in $O\left(\sqrt{N} \cdot T\right)$.

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