This research work on the well-known WM811K wafer map dataset intends to integrate the insights provided by wafer manufacturing techniques and the computing power of quantum convolutional neural networks (QCNN) approach. The work begins with demonstrating high accuracy (𝑹 𝟐 =99.32%) of the classical convolutional neural network (CCNN) consisting of three consecutive convolution layers on the WM-811K dataset. In later part of the paper, optimized for multi-class classification, the applied QCNN model consisting of four qubits used for convolutional and pooling layers exhibit 99.38% accuracy. The QCNN model establishes supremacy over CCNN model by reducing time requirement to train the model for classifying defects on the wafer images.