A resource-efficient quantum convolutional neural network

Yusen Wu

Abstract:

Quantum Convolutional Neural Network (QCNN) has achieved significant
success in solving various complex problems, such as quantum many-body
physics and image recognition. In comparison to the classical Convolutional
Neural Network (CNN) model, the QCNN model requires excellent numerical
performance or efficient computational resources to showcase its potential
quantum advantages, particularly in classical data processing tasks. In this
paper, we propose a computationally resource-efficient QCNN model
referred to as RE-QCNN. Specifically, through a comprehensive analysis of the
complexity associated with the forward and backward propagation processes in
the quantum convolutional layer, our results demonstrate a significant reduction
in computational resources required for this layer compared to the classical CNN
model. Furthermore, our model is numerically benchmarked on recognizing
images from the MNIST and Fashion-MNIST datasets, achieving high accuracy in
these multi-class classification tasks.