Model and algorithm of quantum-inspired neural network with sequence input based on controlled rotation gates

Authors:
Panchi Li;Hong Xiao
Affiliations:
School of Computer & Information Technology, Northeast Petroleum University, Daqing, China 163318;School of Computer & Information Technology, Northeast Petroleum University, Daqing, China 163318
Venue:
Applied Intelligence
Year:
2014

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Abstract

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驴 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.