# A Quantum Convolutional Neural Network for Image Classification Source: [A Quantum Convolutional Neural Network for Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:54:26.560Z Authors: Yanxuan L\\"u, Qing Gao, Jinhu L\\"u, Maciej Ogorza{\\l}ek, Jin Zheng Published: 2021-07-08T06:47:34.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()