Coherent Feed Forward Quantum Neural Network

Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requiring multiple entanglement layers. This methodology not only increases the computational cost of the circuit beyond what is practical on near-term quantum devices but also misleadingly labels these models as neural networks, given their divergence from the structure of a typical feed-forward neural network (FFNN). Moreover, the circuit …

beyond computational cost cs.lg current entanglement feature machine machine learning map methodology multiple near network networks neural network neural networks practical quant-ph quantum quantum neural network quantum neural networks study