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This paper presents a novel parallel Quantum Convolutional Neural Network (QCNN) architecture that enables efficient classical simulation of large-scale QCNNs, demonstrating training on a 128-qubit model for MNIST binary classification.
Proposes a low-overhead error correction technique for quantum convolutional neural networks using bivariate bicycle codes, demonstrating improved learning under realistic noise compared to unprotected QCNNs.