Quantum Random Forest Regression for Indoor Localization

Accurate indoor localization is vital for smart environments and the Internet of Things (IoT) applications. Received signal strength indicator (RSSI)-based methods suffer from multipath fading, signal attenuation, and missing data. To address these issues, we developed quantum random forest indoor localization (QRF-IL), a quantum-inspired machine learning method that combines quantum random forests (QRFs) with weighted centroid regression. Each quantum decision tree in QRF uses a quantum support vector machine (QSVM) with Nyström quantum kernel estimation for efficient and accurate learning. On a public dataset, QRF-IL showed an average localization error of 2.3 m, which was reduced by 9% over a standalone QRF model and 21% over an adaptive path loss model (ADAM).