Policy and Study of Grid Connected PV System

9/13/2022. 1.

snillynvn 'O RIIS* IAINn. Ubaid Ullah, Danyal Maheshwari, Hanna Helene Gloyna, Dr. Maria Begoña Garcia Zapirain.

Introduction to Quantum Computing and Machine Learning Importance and Applications. Used Materials Feature Selection Proposed Quantum Algorithms 3.1 Enhanced Quantum Support Vector Machine E- QSVM 3.2 Quantum Random Forest 3.3 Obtained Results Conclusion and Future Work Acknowledgment.

Introduction to Quantum Computing and Machine Learning.

Importance and Applications. [1] Li, Mingzhang, Shuo Ma, and Zhengrong Liu. "A Novel Method to Detect the Early Warning Signal of Covid-19 Transmission." (2022). [2] Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M. Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science,368(6490), 493-497.

Materials. 5. The data were collected from several public hospital Baja California during the first 8 months of 2020. The used dataset had 92 features and 6550 entries in total. The data consists of 49,7% women and 50,3% men. The dataset contains 1969 patients confirmed covid-19, 4331 typica pneumonia, 46 influenza and 250 other chronic disease. Balanced diabetes dataset is comprised of different features, including, Gender, Age, Fever, Chest pain, abdominal pain, Chronic disease, Background diabetes and Influenza..

Method. 6. The dimensionality has been reduced by using the following process. Too low variance or entropy High Pearson correlation coefficient to another feature (|p| ≥ 0.75) Equality to another feature Information of the feature are contained by another feature Used Random Forest, Support Vector Machine and a Neural Network. Permuted feature importance, calculated baseline metric with all features Assign score to each feature, the lowest score is considering a best feature..

Enhanced Quantum Support Vector Machine E- QSVM. Quantum Support Vector Machine (QSVM) is a quantum version of the Support Vector Machine (SVM) algorithm which uses quantum laws to perform prediction s. Convert classical data to quantum variable. Quantum Feature Map using ZFeatureMap, ZZFeatureMap or PauliFeatureMap, Quantum Entanglement. Quantum Instance, Quantum Simulator, (Statevecter, Qasm) Convert quantum data to classical data. (Measurement)..

Quantum Random Forest (QRF). 8. Covid-19 Dataset ua(x, ea) Ub(X, 0b) Quantum Parameterized Circuit Softmax Optimizer Classified Outcome (O or 1).

Obtained Results. snillynvn 'O RIIS* IAINn. Table 2: Performance evaluation metrics of the proposed model.

5. Conclusion and Future Work. 10. This study presents a robust pre-processing including data balancing and feature selection techniques, where QML algorithms may be used to classify it. The quantum support vector machine is improved by using the hyper parameter tuning, feature mapping and entanglement of various rotation results in better prediction as compare to classical model. The quantum random forest is simulated by using two IBM 10 qubits simulator and run in parallel, which creates an ensemble model and produce promising prediction as compare to classical random forest. In future, the proposed models can be modified more by using different data encoding techniques, such as amplitude encoding and angle encoding techniques..

Acknowledgment. 11. The eVIDA Research Group would like to acknowledge the support of the Basque Government and Osakidetza throughout the project. This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska -Curie..

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