# Quantum Random Forest for the Regression Problem

arXiv: https://arxiv.org/abs/2603.22790 · HTML: https://mlsanity.com/paper/2603.22790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [An Introduction to Quantum Machine Learning for Engineers](https://mlsanity.com/paper/2205.09510/) (2022)
- [Representation of binary classification trees with binary features by quantum circuits](https://mlsanity.com/paper/2108.13207/) (2021)
- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0](https://mlsanity.com/paper/1907.06840/) (2019)
- [The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods](https://mlsanity.com/paper/2112.13346/) (2021)
- [Des-q: a quantum algorithm to provably speedup retraining of decision trees](https://mlsanity.com/paper/2309.09976/) (2023)
- [Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs](https://mlsanity.com/paper/1804.09950/) (2018)
- [Quantum-over-classical Advantage in Solving Multiplayer Games](https://mlsanity.com/paper/2006.06965/) (2020)
- [The Improvement of Decision Tree Construction Algorithm Based On Quantum Heuristic Algorithms](https://mlsanity.com/paper/2212.14725/) (2022)
- [Quantum Circuit for Random Forest Prediction](https://mlsanity.com/paper/2312.16877/) (2023)

## Abstract (arXiv)

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.