Quantum Random Forest for the Regression Problem

Authors: Kamil Khadiev, Liliya Safina Date: 2026-03-24 Paper ID: openalex:2603.22790

Summary

This paper introduces a novel quantum algorithm specifically tailored for the testing and forecasting aspects of the Random Forest model when applied to regression problems. The central claim is that this quantum approach offers a demonstrable advantage over classical methods, primarily in terms of reduced query complexity or overall running time. The contribution lies in mapping the decision structure or aggregation process of the ensemble learning model onto a quantum computational framework to achieve potential speedups. Further details regarding the specific quantum operations and empirical verification against classical benchmarks would be necessary to fully assess its impact.

Key Contributions

• Proposed a quantum algorithm designed to test or forecast the behavior of a classical Random Forest model for regression tasks.
• The quantum algorithm is claimed to achieve superior query complexity or running time compared to its classical counterpart.

Limitations

The abstract provides no details on the specific quantum hardware requirements, implementation specifics, or empirical results against classical Random Forest for regression.

Links

• Abstract
• PDF