[2603.22790] Quantum Random Forest for the Regression Problem

Quantum Physics arXiv:2603.22790 (quant-ph) [Submitted on 24 Mar 2026] Title:Quantum Random Forest for the Regression Problem Authors:Kamil Khadiev, Liliya Safina View a PDF of the paper titled Quantum Random Forest for the Regression Problem, by Kamil Khadiev and Liliya Safina View PDF 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. Comments: 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 arXiv-issued DOI via DataCite (pending registration) Submission history From: Kamil Khadiev [view email] [v1] Tue, 24 Mar 2026 04:27:17 UTC (141 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Random Forest for the Regression Problem, by Kamil Khadiev and Liliya SafinaView PDFTeX Source 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 ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibli...