Hierarchical image partitioning plus binary-tree density-matrix merging enables classical simulation of 128-qubit QCNNs for MNIST binary classification without accuracy loss.
Splitting and parallelizing of quantum convolutional neural networks for learning translationally symmetric data,
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A Novel Parallel QCNN Architecture with Efficient Classical Simulability
Hierarchical image partitioning plus binary-tree density-matrix merging enables classical simulation of 128-qubit QCNNs for MNIST binary classification without accuracy loss.