A Novel Parallel QCNN Architecture with Efficient Classical Simulability
Using a novel architecture inspired by previous QCNN and classical convolutional neural network (CNN) implementations, we use a hierarchical partitioning approach to implement a QCNN circuit that can be approximated and simulated efficiently on a classical machine for a large problem. First, the original image is partitioned such that each process handles a smaller portion of the image, which is encoded into independent states. Then, these partitions merge and combine, resulting in states that contain information from both partitions while halving the number of processes.
- ▪Using a novel architecture inspired by previous QCNN and classical convolutional neural network (CNN) implementations, we use a hierarchical partitioning approach to implement a QCNN circuit that can be approximated and simulated efficientl
- ▪First, the original image is partitioned such that each process handles a smaller portion of the image, which is encoded into independent states.
- ▪Then, these partitions merge and combine, resulting in states that contain information from both partitions while halving the number of processes.
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Quantum Physics arXiv:2607.08928 (quant-ph) [Submitted on 9 Jul 2026] Title:A Novel Parallel QCNN Architecture with Efficient Classical Simulability Authors:Lawrence Nguyen, Hiu Yung Wong View a PDF of the paper titled A Novel Parallel QCNN Architecture with Efficient Classical Simulability, by Lawrence Nguyen and Hiu Yung Wong View PDF HTML (experimental) Abstract:This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset.
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