A Quantum Convolutional Neural Network for Image Classification

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.

Techniques need to be double checked

References in corpus (7)

• Quantum algorithm for solving linear systems of equations
• The quest for a Quantum Neural Network
• Simulating a perceptron on a quantum computer
• Circuit-Based Quantum Random Access Memory for Classical Data
• An Introduction to Cartan's KAK Decomposition for QC Programmers
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
• Toward Trainability of Quantum Neural Networks