Multi-Class Quantum Convolutional Neural Networks

arXiv:2404.12741v1 Announce Type: cross
Abstract: 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 …

abstract arxiv class classification convolutional neural network convolutional neural networks cross-entropy cs.et cs.lg data entropy information network networks neural network neural networks optimization pipeline process quant-ph quantum retrieval search type work