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

TL;DR

This paper introduces a quantum algorithm for ensemble binary classification that significantly reduces prediction time from linear to square root scale relative to the number of classifiers, leveraging quantum speedup.

Contribution

It proposes a novel quantum prediction algorithm for ensemble classifiers that improves runtime efficiency over classical methods.

Findings

Quantum prediction algorithm achieves O(√N·T) complexity.

Classical ensemble prediction runs in O(N·T) time.

Quantum approach offers faster prediction for large ensembles.

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 $O\left(\sqrt{N} \cdot T\right)$.