Publications
Talk · International Conference on Quantum Communications, Networking, and Computing · April 6, 2026
Quantum Random Forest for the Regression Problem
Kamil Ravilevich Khadiev, Liliya Safina
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.
Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum many-body systemsquantum machine learningregression analysisquantum algorithms
More by Kamil Khadiev
- Quantum Hashing Circuit Optimization for Arbitrary Qubit Connectivity Graphs Based on 1-Covering PatharXiv · 2026
- Quantum k-means based on Quantum Local Sensitive Hashing with Application to Irrigation Machine Sensors Data ClusteringIEEE International Conference on Smart Computing Workshops and Other Affiliated Events · 2026
- Circuits of Quantum Hashing and Quantum Fourier Transform for a Cactus as a Qubit Connectivity GrapharXiv · 2026
- Quantum Circuit for Quantum Fourier Transform for Arbitrary Qubit Connectivity Graphs Based on 1-Cover PathInternational Conference on Quantum Communications, Networking, and Computing · 2026 · Citations: 1