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Learning performance of neuron model based on quantum superposition

Concerns the use of quantum computer methods to develop a distributed and strongly connectionist system that achieves parallel and fast information processing. We have proposed a qubit-like neuron model based on quantum mechanics and constructed the quantum backpropagation learning rule (QBP). In this paper, we show our improved QBP neural network model and discuss its performance on solving the 4 bit parity check problem, the function and the gray-scale pattern identification problem. Then, we find our model is more excellent than the conventional one in information processing efficiency.