Learning performance of neuron model based on quantum superposition

Author

Kouda, Noriaki ; Matsui, Nobuyuki ; Nishimura, Haruhiko

Author_Institution

Fac. of Eng., Himeji Inst. of Technol., Hyogo, Japan

fYear

2000

fDate

2000

Firstpage

112

Lastpage

117

Abstract

Concerns the use of quantum computer methods to develop a distributed and strongly connectionist system that achieves parallel and fast information processing. We have proposed a qubit-like neuron model based on quantum mechanics and constructed the quantum backpropagation learning rule (QBP). In this paper, we show our improved QBP neural network model and discuss its performance on solving the 4 bit parity check problem, the function and the gray-scale pattern identification problem. Then, we find our model is more excellent than the conventional one in information processing efficiency

Keywords

backpropagation; neural nets; quantum computing; 4 bit parity check problem; QBP; distributed strongly connectionist system; gray-scale pattern identification problem; learning performance; neural network; neuron model; parallel fast information processing; quantum back propagation; quantum backpropagation learning rule; quantum computer; quantum mechanics; quantum superposition; qubit-like neuron model; Backpropagation; Concurrent computing; Distributed computing; Gray-scale; Information processing; Neural networks; Neurons; Parity check codes; Quantum computing; Quantum mechanics;

fLanguage

English

Publisher

ieee

Conference_Titel

Robot and Human Interactive Communication, 2000. RO-MAN 2000. Proceedings. 9th IEEE International Workshop on

Conference_Location

Osaka

Print_ISBN

0-7803-6273-X

Type

conf

DOI

10.1109/ROMAN.2000.892480

Filename

892480