Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data

arXiv:2306.07331v3 Announce Type: replace-cross
Abstract: The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data learning, limiting its practical applications in large-scale problems. To alleviate this requirement, we propose a novel architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data to design an efficient model. This architecture draws inspiration from …

abstract advantages applications arxiv convolutional neural network convolutional neural networks cs.lg data machine machine learning network networks neural network neural networks practical qml quant-ph quantum type