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In this paper, a neural networks model for quantum computer is proposed. The core of this model is quantum neuron. Firstly, the inner product of the input qubits and the weight qubits is mapped to the phase of the control qubits in the neuron by the swap test technology, and then these phases are obtained by the phase estimation method, which are further used as the phase of the output qubit in the neuron. In this way, the mapping of input qubits to output qubit in quantum neuron is completed. The quantum neurons mentioned above can be used to construct quantum neural networks. In this paper, the quantum circuit for each operation step are given. The simulation results on the classic computer verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

To enhance the approximation and generalization ability of artificial neural networks (ANNs) by employing the principle of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state $$|1\rangle $$ in the target qubit. Then a quantum-inspired neural networks (QINN) is designed by employing the quantum-inspired neurons to the hidden layer and the common neurons to the output layer. The algorithm of QINN is derived by employing the Levenberg-Marquardt algorithm. Simulation results of some benchmark problems show that, under a certain condition, the QINN is obviously superior to the classical ANN. [ABSTRACT FROM AUTHOR]

To enhance the approximation and generalization ability of classical artificial neural network (ANN) by employing the principles of quantum computation, a quantum-inspired neuron based on controlled-rotation gate is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-rotation gate after being rotated by the quantum rotation gates, control the target qubit for rotation. The model output is described by the probability amplitude of state |1〉 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the quantum-inspired neurons to the hidden layer and the classical neurons to the output layer. An algorithm of QNNSI is derived by employing the Levenberg-Marquardt algorithm. Experimental results of some benchmark problems show that, under a certain condition, the QNNSI is obviously superior to the ANN. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]