Model and Training of QNN with Weight

Authors:
Zhou Rigui;Jiang Nan;Ding Qiulin
Affiliations:
Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 210016;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 210016;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 210016
Venue:
Neural Processing Letters
Year:
2006

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Abstract

Quantum Neural Network (QNN) is a burgeoning new field built upon the combination of classical neural networks and quantum computations, which has many problems needed to solve. Where the learning of the network weight vector is an issue must be settled to develop QNN. Upon the analysis of the Grover's quantum algorithm, a model of QNN with weight vector and a training method for it are proposed in this paper. It can be shown that this model and method work in quantum mechanism. Results on the data set show that this network model can deal with some classical problem such as XOR problem and the proposed weight updating algorithm based on the Grover always can learn training examples in a certain percentage, despiting it has not been proven to excel classical learning algorithm in performance. It yet has some advantages over classical counterpart.