Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantum Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster …
Guardians of the Quantum GAN
Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and…
Quantum Machine LearningQuantum Convolutional Neural Networks for Groundwater Heat Plume Prediction: A Surrogate Modeling Approach
Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In this work, we explore a Quantum Convolutional Neural Network (QCNN) as …
Quantum Machine LearningOn-board classification of underwater images using hybrid classical-quantum CNN based method
Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques fo…
Autonomous VehiclesGPUHybrid Machine Learningimage-classification+5Multi-task Temporal Convolutional Network for Predicting Water Quality Sensor
Predicting the trend of water quality is essential in environmental management decision support systems. Despite various data-driven models in water quality prediction, most studies focus on predicting a single water qua…
ManagementTime Series Prediction