  Classification is particularly relevant to Information Retrieval, as it is
used in various subtasks of the search pipeline. In this work, we propose a
quantum convolutional neural network (QCNN) for multi-class classification of
classical data. The model is implemented using PennyLane. The optimization
process is conducted by minimizing the cross-entropy loss through parameterized
quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4,
6, 8 and 10 classes. The results show that with 4 classes, the performance is
slightly lower compared to the classical CNN, while with a higher number of
classes, the QCNN outperforms the classical neural network.
