Z feature map quantum neural networks reach R^2 0.92-0.93 for wind power prediction in simulation, but the paper's claim that they outperform classical methods is contradicted by its MAE results.
Quantum machine learning for anomaly detection in consumer electronics,
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Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations
Z feature map quantum neural networks reach R^2 0.92-0.93 for wind power prediction in simulation, but the paper's claim that they outperform classical methods is contradicted by its MAE results.