Research On Quantum Neural Network And Its Applications

Quantum neural network(QNN) is a new neural networks calculation model based on the combinations of traditional artificial neural networks(ANN) theory and quantum computation theory. In this dissertation, the theories(such as superposition,entangled, interference of quantum state and quantum parallel computing etc.)of quantum computation are introduced into traditional neural networks, have overcome some inherent limitations of those traditional artificial neural networks to improve its ability of information processing. The present paper takes based on the applied research of quantum neural network as a starting point,to construct a new QNN through reserch the quantum theory and already existed quantum neural network models,then makes an application in pattern recognition. The simulation results of the two QNNs show that the quantum neural network not only have good foreground but also indicate it's superiority and potential in solving pattern recognition problems. Over all the main content of this dissertation includes the following three aspects.(1)An analysis and research of quantum computation and corresponding quantum theory,this paper expounds in detail the reasons of high-powered quantum computation and inherent defects of traditional neural network.(2)Make a theoretical analysis to prove the possibility of neural computation in the quantum system,and the advantage of quantum neural computation by comparing the quantum computation and the related concepts of neural computation. The characteristics, architectures and learning algorithms of several QNN models are analyzed in detail, such as quantum neuron,quantum associative memory, quantum self-organization feature mapping networks and many universes.(3)On the research and use for reference of the superposition state of quantum,we construct a three-layered quantum neural networks model based on multi-level transfer function and apply it to pattern recognition with overlapping classes due to the vagueness and uncertainty in the superposition state of quantum. We make an experiment of comparing the QNN and the traditonal neural networks,and the simulation results show that the recognition rate of the QNN increases significantly. (4)Then we construct a three-layered quantum neural networks based on universal quantum gates units. This QNN realize quantum neural computation by using universal quantum gates cell as neural network's activation functions. The English letters recognition system is based on the QNN, which can recognise capital or small letters. The experimental results indicate the QNN achieves excellent performance in terms of recognition rates and a good generalization capacity. Furthermore, this QNN model has the potential to be implemented with quantum hardware.