Quantum Convolutional Neural Network On Scale Chaology

In this paper we propose a quantum convolutional neural network(QCNN) based chaotic metric classification for chaology scaling-down images in the tensorflow quantum framework. The chaology image is properly downscaled with the measurement of spatial occupancy and granularity introduced by fractal dimension and the dimensionality of the overall input space is reduce before it is fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. The experimental results show that QCNN, Hybrid QCNN and Hybrid QCNN with multiple quantum filters have achieved relatively high accuracies on more than 94%, and convergence of these three kinds of QCNN classifier has a small fluctuation which indicates that through this scale transformation the discrepancy among the different quantum hybrid classifiers is minimized to some extent.