This paper extends the idea of creating a Quantum Machine Learning classifier and applying it to real weather data from the weather station behind the Pa Sak Jonlasit Dam.A systematic study of classical features and optimizers with different iterations of parametrized circuits is presented.The study of the weather behind the dam is based on weather data from 2016 to 2022 as a training dataset.Classification is one problem that can be effectively solved with quantum gates.There are several types of classifiers in the quantum domain, such as Quantum Support Vector Machine (QSVM) with kernel approximation, Quantum Neural Networks (QNN), and Variational Quantum Classification (VQC).According to the experiments conducted using Qiskit, an open-source software development kit developed by IBM, Quantum Support Vector Machine (QSVM), Quantum Neural Network (QNN), and Variable Quantum Classification (VQC) achieved accuracy 85.3%, 52.1%, and 70.1% respectively.Testing their performance on a test dataset would be interesting, even in these small examples.
This paper extends the idea of creating a Quantum Machine Learning classifier and applying it to real weather data from the weather station behind the Pa Sak Jonlasit Dam.A systematic study of classical features and optimizers with different iterations of parametrized circuits is presented.The study of the weather behind the dam is based on weather data from 2016 to 2022 as a training dataset.Classification is one problem that can be effectively solved with quantum gates.There are several types of classifiers in the quantum domain, such as Quantum Support Vector Machine (QSVM) with kernel approximation, Quantum Neural Networks (QNN), and Variational Quantum Classification (VQC).According to the experiments conducted using Qiskit, an open-source software development kit developed by IBM, Quantum Support Vector Machine (QSVM), Quantum Neural Network (QNN), and Variable Quantum Classification (VQC) achieved accuracy 85.3%, 52.1%, and 70.1% respectively.Testing their performance on a test dataset would be interesting, even in these small examples.
Weather forecasting is a major challenge because of the unpredictable nature of atmospheric systems and the large number of interacting variables. Although traditional machine learning models have been used for weather prediction, they cannot capture the complex, nonlinear dynamics of weather. Recently, Quantum Machine Learning (QML) shows ability to improve computational speed and pattern recognition. Its practical application in weather prediction, however, is not well exploited. This work methodically analyzes QML algorithms for predicting weather states and compares their predictive performance with that of classical machine learning. We assembled extensive hourly meteorological data for Sylhet City, Bangladesh (24.9172∘N,91.8319∘E) over 185 days from Microsoft and IBM weather sites, totaling 4,504 data points. The dataset combines the main atmospheric parameters as temperature, real feel temperature, humidity, precipitation probability, visibility, dew point, and air pressure, from Microsoft and IBM weather sites. Four QML algorithms, including Quantum Random Forest (QRF), Quantum Decision Tree (QDT), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) are trained and tested for five weather state classification, and compared to their classical counterparts named SVM, Random Forest, Decision Tree, and Neural Network. All quantum experiments were run on Qiskit simulators due to current hardware limitations. Results show that classical models performed slightly better than quantum models, and the difference was not very large.
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.
Quantum computer introduces a novel approach to process information. In quantum information processing, the law of quantum mechanics is applied to solve many practical computational problems. Classification is one such problem that can be resolved efficiently with the gate model quantum computer. There are several types of classifiers available in quantum domain, such as- variational quantum classifier (VQC), Quantum Support Vector Machine (QSVM) with Kernel Approximation, Hybrid Quantum Neural Network (QNN) etc. However, in this study, the mathematical similarities between VQC and classical support vector machine (SVM) and the components of the VQC are analyzed to optimize the performance of the classifier. For the convenience of the study, publicly available datasets, such as- IRIS dataset and Breast cancer dataset, are used in the experiments. IRIS dataset is brought into play for the testing and breast cancer dataset dimension reduced by Principle component analysis (PCA) is for validity test of the optimized VQC. After studying the VQC components in detail, it is found that the optimized VQC outperforms some of the classical machine learning algorithms or sometimes works as similar as classical SVM. The optimized VQC algorithm classifies IRIS dataset with 100% of accuracy and PCA dimension reduced Breast cancer dataset with 90% of accuracy. All of these studies are conducted with the help of Qiskit- an open-source software development kit (SKD) which is developed by IBM. So, the quantum device is considered to be ideal in every experiment.
Quantum Computing is a new way to create a different type of computer system from Quantum Mechanics. Quantum machine learning is one of the applications took the benefit from qauntum computers. In this experiment, we use the IBM Qiskit library to study how well a quantum machine learning classifier works. We made two different colored dots to represent the two-class dataset. The quantum circuit can be collected from the Qiskit library. The 1stand 2ndorder feature map schemes operated by oracle quantum oracle functions are applied before making a classification. Finally, we can call the quantum support vector machine or QSVM function to classify data. Kernel matrix from the studied qauntum model shows that quantum computing can be used to clearly classify classes of the dataset with 100 percent accuracy.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.