The development of layer-wise learning for quantum neural networks (QNNs) represents a significant milestone in the intersection of quantum computing and machine learning. The integration of quantum computing principles with neural network architectures aims to exploit the computational advantages of quantum mechanics, such as superposition and entanglement, to enhance the performance of machine learning models.