A Novel Parallel QCNN Architecture with Efficient Classical Simulability
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. 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, re...
| Authors | Lawrence Nguyen, Hiu Yung Wong |
|---|---|
| Primary category | quant-ph |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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