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A New Method of Image Compression Based on Quantum Neural Network

机译:基于量子神经网络的图像压缩新方法

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In this paper we combine with quantum neural networks and image compression using Quantum Gates as the basic unit of quantum computing neuron model, and establish a three layer Quantum Back Propagation Network model, then the model is used for realizing image compression and reconstruction. Since the initial weights of neural networks were slow convergence, we use Genetic Algorithm (GA) to optimize the neural network weights, and present a mechanism called clamping to improve the genetic algorithm. Finally, we combined the Genetic Algorithm with quantum neural networks to finish image compression. Through an experiment we can see the superiority of the improved algorithm.
机译:本文将量子门作为量子计算神经元模型的基本单元,结合量子神经网络和图像压缩技术,建立了三层量子反向传播网络模型,然后将该模型用于图像压缩和重构。由于神经网络的初始权重是缓慢收敛的,因此我们使用遗传算法(GA)来优化神经网络的权重,并提出了一种称为钳位的机制来改进遗传算法。最后,我们将遗传算法与量子神经网络结合起来以完成图像压缩。通过实验我们可以看到改进算法的优越性。

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