Predicting water quality using quantum machine learning: The case of the umgeni catchment (U20A) study region

The assessment of water quality has become increasingly vital for maintaining the ecological balance and ensuring public safety across global water systems. This study examines the application of Quantum Machine Learning (QML) techniques in a real-world setting to predict water quality in the U20A region of the Umgeni Catchment, Durban, South Africa. We implemented the Quantum Support Vector Classifier (QSVC) and Quantum Neural Network (QNN) on a field-collected dataset. Our results demonstrate that the QSVC is more practical to implement and yields superior performance, achieving 75 % accuracy with polynomial and radial basis function kernels. In contrast, the QNN encountered persistent convergence issues, including the “dead neuron” problem, despite various optimization strategies. The findings provide a pragmatic framework for environmental monitoring applications, suggesting that QSVC offers a more viable near-term quantum approach for water quality classification tasks with imbalanced, real-world data.