In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three kernels: Linear, polynomial, and radial basis function (RBF), and it was shown that the polynomial and RBF kernels had exactly the same performance. The QNN model was applied using different optimizers, learning rates, noise on the circuit components, and weight initializations were considered, but the QNN persistently ran into the dead neuron problem. Thus, the QNN was compared only by accraucy and loss, and it was shown that with the Adam optimizer, the model has the best performance, however, still less than the QSVC.

本研究针对南非德班U20A地区水质研究中的实际应用，探讨了量子机器学习技术。通过应用量子支持向量分类器（QSVC）和量子神经网络（QNN），发现QSVC更易于实现并且准确率更高，展示了在水质预测方面的潜力和优势。

利用量子机器学习预测水质：乌姆根尼流域（U20A）研究区域的案例