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.

翻译：分类问题在信息检索中尤为相关，因其广泛应用于搜索管道的各个子任务中。本文提出一种用于经典数据多类分类的量子卷积神经网络（QCNN）。该模型基于 PennyLane 实现，通过参数化量子电路优化最小化交叉熵损失以驱动优化过程。我们在 MNIST 数据集上对 4 类、6 类、8 类和 10 类分类任务进行了测试。结果表明，在 4 类任务中，QCNN 性能略低于经典 CNN；但随着类别数量的增加，QCNN 的表现优于经典神经网络。