Model and algorithm of quantum-inspired neural network with sequence input based on controlled rotation gates
Panchi Li, Hong Yu Xiao · Applied Intelligence · 2013
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.