Chaotic Time Series Prediction by Qubit Neural Network with Complex-Valued Representation
Chaotic Time Series Prediction by Qubit Neural Network with Complex-Valued Representation
复制标题
具有复值表示的量子位神经网络的混沌时间序列预测
DOI：
10.1109/sice.2016.7749232
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
T. Isokawa
中科院分区：
文献类型：
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作者：
T. Ueguchi;N. Matsui;T. Isokawa
量子比特神经网络(QNN)是一种结合了量子计算和表示的神经网络。量子神经网络是由一组量子比特神经元模型构成的，其内部状态是量子比特态的相干叠加。通过对三个动力系统产生混沌时间序列的著名Lorentz吸引子的预测，对QNN的性能进行了评估。实验结果表明，与传统的(实数)神经网络相比，QNN能够更准确地预测时间序列。
A qubit neural network (QNN) is a neural network that incorporates the quantum computing and representation. QNN is constructed from a set of qubit neuron model, of which internal state is a coherent superposition of qubit states. This paper evaluates the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can predict time series more precisely, compared with conventional (real-valued) neural networks.