A Quantum Convolutional Neural Network for Image Classification

TL;DR

This paper introduces a Quantum Convolutional Neural Network (QCNN) that leverages quantum computing to enhance image classification, aiming to overcome classical neural network resource limitations.

Contribution

It proposes a novel QCNN model based on quantum circuits, bridging quantum computing with classical CNN structures for improved image processing.

Findings

Effective on MNIST dataset in simulations

Potential to accelerate classical machine learning tasks

Quantum circuits enable similar structure to classical CNNs

Abstract

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.