Abstract: A novel Elman neural model with hybrid quantum gate structure and a quantized extended-gradient backpropagation (BP) training algorithm are proposed for improving the performance of the conventional Elman network. The novel model is comprised of qubit neurons and classical neurons. The quantum map layer is employed to address the pattern mismatch between the context layer and the input layer. The complementary relationships between the outputs of qubit neurons and the quantum gate parameters are applied to improve the updated ability of the conventional Elman network. The learning rate is adaptively adjusted by the searching and convergent learning strategy, which makes the new neural model achieving convergence with high speed. The context-layer weights are extended into the hidden-layer weights matrix for obtaining the extra gradient information, such that the context-layer patterns match the input-layer patterns with high level. The numerical experiments are carried out to verify the theoretical results and clearly show that the hybrid quantized Elman network using quantized training offers a good performance in terms of both weight convergence and generalization ability.