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Quantum Machine Learning

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

arXiv

Authors: Kamil Khadiev, Liliia Safina

Year

2021

Paper ID

40254

Status

Preprint

Abstract Read

~2 min

Abstract Words

120

Citations

N/A

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 Oleft\(N cdot T right\). We propose an algorithm which works in Oleft\(sqrt{N} cdot Tright\).

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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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2112.13346 [2] arXiv https://arxiv.org/abs/2112.13346 [3] Publisher https://arxiv.org/abs/2112.13346

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