A hybrid quantum-inspired neural networks with sequence inputs

To enhance the performance of classical neural networks, a quantum-inspired neural networks model based on the controlled-Hadamard gates is proposed. In this model, the inputs are discrete sequences described by a matrix where the number of rows is equal to the number of input nodes, and the number of columns is equal to the sequence length. This model includes three layers, in which the hidden layer consists of quantum neurons, and the output layer consists of classical neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-Hadamard gates. A learning algorithm is presented in detail according to the basic principles of quantum computation. The characteristics of input sequence can be effectively obtained from both breadth and depth. The experimental results show that, when the number of input nodes is closer to the sequence length, the proposed model is obviously superior to the BP neural networks.