Sequence Input-Based Quantum-Inspired Neural Networks with Applications

Abstract

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 $$|1\rangle $$ | 1 ? 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.