Abstract

This paper introduces a quantum convolutional neural network model that is implementable on real quantum circuits. Three basic blocks, including the quantum encoding block, the model design block and the parameter tuning block, are designed to formulate the quantum convolutional neural network framework. Simulation results on the MNIST dataset are presented to demonstrate the representation, learning, and optimization capacity of the proposed model.

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