A QCNN Algorithm Deployed on A Quantum Computer

Abstract

CNN (Convolutional neural network) is an excellent machine learning model, especially in image recognition, where they have excellent results. However, it is difficult to learn effectively if the given dimensionality of the data or model is too large. QCNN QCNN (Quantum Convolutional Neural Network) offer new solutions to the problems faced by CNN using a quantum computing environment and provide directions for improving the performance of existing learning models. However, most of the current QCNN models are only simulation programs running on a classical computer and cannot actually run on a quantum computer. In this paper, we propose a QCNN model with a quantum encoding circuit that can encode classical data into quantum data, allowing the QCNN to run on quantum computers. Firstly, we introduce the method of quantum state amplitude encoding to represent the high-dimensional classical data into the form of quantum states, constructing a bridge between traditional data and quantum state transformation, while significantly reducing the dimensionality of the processed data. Secondly, we construct the QCNN model and analyze how to implement the image recognition function by QCNN network and the optimization method of the model. Finally, we validate the learning effect of the model using MNIST handwritten digits and compare and discuss the learning effect of QCNN with classical models.