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.

arXiv commentTechniques need to be double checked

References in corpus (18)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• The theory of variational hybrid quantum-classical algorithms
• Quantum support vector machine for big data classification
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Evaluating analytic gradients on quantum hardware
• The quest for a Quantum Neural Network
• Quantum advantage with shallow circuits
• Minimal Universal Two-qubit Quantum Circuits
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• 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
• End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics