Quantum backpropagation is a method used in training quantum neural networks (QNNs) that leverages the principles of quantum mechanics to optimize the weights of the network. It adapts the classical backpropagation algorithm by utilizing quantum states and operations, allowing for potentially faster convergence and improved efficiency in training compared to traditional methods. This technique plays a crucial role in both the modeling of quantum neurons and the overall training strategies employed for QNNs.
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