Quantum Random Forest for the Regression Problem

TL;DR

This paper introduces a quantum algorithm for regression with Random Forests that improves efficiency over classical methods, offering faster forecasting capabilities.

Contribution

It presents a novel quantum algorithm for Random Forest regression that reduces query complexity and enhances computational efficiency.

Findings

Quantum algorithm outperforms classical in efficiency

Reduced query complexity in regression tasks

Faster forecasting with quantum methods

Abstract

The Random Forest model is one of the popular models of Machine learning. We present a quantum algorithm for testing (forecasting) process of the Random Forest machine learning model for the Regression problem. The presented algorithm is more efficient (in terms of query complexity or running time) than the classical counterpart.