Quantum Random Forest for the Regression Problem
This work addresses the regression problem for machine learning practitioners by offering a quantum speedup, though it appears incremental as it adapts an existing model to a quantum context.
The authors tackled the regression problem by developing a quantum algorithm for the Random Forest model's forecasting process, resulting in improved efficiency in terms of query complexity or running time compared to classical methods.
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.