单层量子感知器网络及其性能

MONOLAYER QUANTUM PERCEPTRON NETWORK AND ITS PERFORMANCE

目前人工智能的体系结构普遍比较复杂,所以它的广泛应用受到了很大的限制。利用量子计算的一些优点特别是量子并行计算特性提出一个单层量子感知器网络,该网络充分利用量子相位,使得它具有传统的单层感知器所无法具有的计算能力。对单层量子感知器进行实例分析、性能分析和仿真实验,表明单神经元量子感知器能实现单神经元经典感知器无法实现的XOR功能。即简单的网络结构实现了相对复杂的网络功能,这一特点有利于降低网络体系结构的复杂性,它必将对人工智能和控制领域的研究产生重大的影响。

At present, it is usual that the architecture of artificial intelligence is relatively complex, this has greatly restricted its wide application. By using some merits of quantum computation, especially the quantum parallelism characteristic, a monolayer quantum perceptron network is presented in this paper. The network utilizes quantum phase adequately to have made. it with the computing power that the conventional perceptron is unable to possess. The case analysis, performance analysis and simulation experiments executed on the monolayer quantum perceptron shown that the single neuron quantum perceptron can realize XOR function which is unrealizable by a classical perceptron of single neuron. That means the simple network achieves relatively complicated network function and this benefits to reduce the complexity of the network architecture, it will also throw heavy influence on the field of artificial intelligence and control engineering.