Quantum Random Forest for the Regression Problem

2026-03-24

AI Summary

This work proposes a novel quantum computing–based prediction algorithm for random forest regression to address its computational inefficiency during the inference phase. By integrating quantum subroutines into the prediction process, the method leverages quantum parallelism to reduce query complexity. Theoretical analysis demonstrates that the proposed algorithm achieves significantly improved runtime performance over classical implementations during prediction, while preserving model accuracy. This advancement offers a promising and efficient pathway for handling large-scale regression tasks, marking the first application of quantum-enhanced techniques to accelerate random forest inference.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Quantum Random Forest
• Regression Problem
• Machine Learning
• Query Complexity

Innovation

• Quantum Random Forest
• Regression
• Quantum Algorithm
• Query Complexity
• Machine Learning