Quantum Random Forest for the Regression Problem

Quantum Physics [Submitted on 24 Mar 2026] Quantum Random Forest for the Regression Problem Kamil Khadiev, Liliya Safina 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. Comments: Accepted in Quantum Computing - Artificial Intelligence for Industry Applications and Scientific Discovery A Workshop at the IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC) 2026 Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.22790 [quant-ph]   (or arXiv:2603.22790v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.22790 Focus to learn more Submission history From: Kamil Khadiev [view email] [v1] Tue, 24 Mar 2026 04:27:17 UTC (141 KB) Access Paper: view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)