Quantum convolutional neural network: a hybrid quantum-classical approach for iris dataset classification

HIGHLIGHTS

• What: Safriandono et_al emphasized the impact of quantum feature engineering in addressing class imbalance and enhancing classification accuracy, aligning with the goals of this study to optimize feature extraction. Their concept has directly influenced the hybrid QCNN architecture employed in this study, showcasing how quantum circuits can enhance classical neural_networks for tasks like classification. While achieving quantum supremacy is not the goal of this research, their work underlines the transformative potential of quantum algorithms, particularly in feature extraction tasks within machine_learning. This research shows the application of quantum feature encoding and classical neural_network integration within a QCNN . . .
  

   

  

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