Variational Quantum Regression Application in Modeling Monthly River Discharge
Zhen Liu, Alina Bărbulescu · Water · 2025
In the framework of efficient water resources management, the hydrological forecast is the basis of the pertinent management of water resources. Therefore, this study applies the variational quantum regression (VQR), a novel machine learning approach inspired by quantum computing principles, to the series of water discharges from a river in Romania. The models were evaluated against the quantum neural network (QNN) and other classic artificial intelligence (AI) outputs on the same dataset. Performance was assessed based on the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). VQR outperformed classical neural networks and hybrid models with respect to MSE and MAE, demonstrating superior accuracy and generalization capability. Notably, the models exhibited exceptional skill in capturing monthly maxima—an area where other models often struggle, underscoring the potential of VQR as a powerful and reliable tool for hydrological forecasting, particularly in the context of nonlinear and high-variability data series.