Quantum Random Forest for the Regression Problem
Not on the map. Why?
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 47 references that are in the map. First posted on arXiv 24 Mar 2026.
Builds on
11 of 47 references.
References · 11
- 2015A Random Forest Guided Tourcited by 168
- 2022An Introduction to Quantum Machine Learning for Engineerscited by 22
- 2021Representation of binary classification trees with binary features by quantum circuitscited by 7
- 2022A kernel-based quantum random forest for improved classificationcited by 7
- 2019The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0cited by 6
- 2021The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methodscited by 4
- 2023Des-q: a quantum algorithm to provably speedup retraining of decision treescited by 4
- 2018Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGscited by 2
- 2020Quantum-over-classical Advantage in Solving Multiplayer Gamescited by 2
- 2022The Improvement of Decision Tree Construction Algorithm Based On Quantum Heuristic Algorithmscited by 1
- 2023Quantum Circuit for Random Forest Predictioncited by 1
Built on by
Not cited yet.