摘要: 针对量子神经网络的训练结果易陷入局部极小值的问题,将Levenberg-Marquardt(LM)算法引入到原训练算法中,从而提高网络收敛速度与训练效果。并通过改进原训练算法的迭代步骤,解决训练过程中网络权值与量子间隔不同的目标函数相互冲突引起的输出均方误差和波动的问题。实验结果表明,相比原训练算法,引入LM后的训练算法可以大幅减少迭代次数,显著降低网络收敛值,提高量子神经网络的分类效果。
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