Abstract
To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles 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 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.
Highlights
Many neuro-physiological experiments indicate that the information processing character of the biological nerve system mainly includes the following eight aspects: the spatial aggregation, the multi-factor aggregation, the temporal cumulative effect, the activation threshold characteristic, self-adaptability, exciting and restraining characteristics, delay characteristics, conduction and output characteristics [1]
In order to fully simulate biological neuronal information processing mechanisms and to enhance the approximation and generalization ability of artificial neural network (ANN), we proposed a quantum-inspired neural network model with sequence input, called quantum-inspired neural network with sequence input (QNNSI)
{ } neurons with sequence input, and the output layer consists of classical neurons
Summary
Many neuro-physiological experiments indicate that the information processing character of the biological nerve system mainly includes the following eight aspects: the spatial aggregation, the multi-factor aggregation, the temporal cumulative effect, the activation threshold characteristic, self-adaptability, exciting and restraining characteristics, delay characteristics, conduction and output characteristics [1]. (2015) Quantum-Inspired Neural Network with Sequence Input. C. Li neuron model, classical ANN preferably simulates voluminous biological neurons’ characteristics such as the spatial weight aggregation, self-adaptability, conduction and output, but it does not fully incorporate temporal cumulative effect because the outputs of ANN depend only on the inputs at the moment regardless of the prior moment. From all the above QNN models, like M-P neurons, it does not fully incorporate temporal cumulative effect because a single input sample is either irrelative to time or relative to a moment instead of a period of time. In order to fully simulate biological neuronal information processing mechanisms and to enhance the approximation and generalization ability of ANN, we proposed a quantum-inspired neural network model with sequence input, called QNNSI. The proposed approach is utilized to the time series prediction for Mackey-Glass, and the experimental results indicate that, under a certain condition, the QNNSI is obviously superior to the common ANN
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
