| 引用本文: | 李盼池.一种量子神经网络模型学习算法及应用[J].控制理论与应用,2009,26(5):531~534.[点击复制] |
| LI Pan-chi.A learning algorithm and its applications to the quantum neural network model[J].Control Theory and Technology,2009,26(5):531~534.[点击复制] |
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| 一种量子神经网络模型学习算法及应用 |
| A learning algorithm and its applications to the quantum neural network model |
| 摘要点击 2410 全文点击 1779 投稿时间：2007-06-25 修订日期：2008-12-03 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.5.011 |
| 2009,26(5):531-534 |
| 中文关键词 量子计算 量子神经元 量子神经网络 超线性收敛 |
| 英文关键词 quantum computing quantum neuron quantum neural network super-linear convergence |
| 基金项目 国家自然科学基金资助项目(60773065). |
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| 中文摘要 |
| 提出一种量子神经网络模型及学习算法. 首先基于生物神经元信息处理机制和量子计算原理构造出一种量子神经元, 该神经元由加权、聚合、活化、激励四部分组成. 然后由量子神经元构造出三层量子神经网络模型, 其输入和输出为实值向量, 权值和活性值为量子比特. 基于梯度下降法构造了该模型的超线性收敛学习算法. 通过模式识别和函数逼近两种仿真结果表明该模型及算法是有效的. |
| 英文摘要 |
| A quantum neural network model and its learning algorithm are presented. According to the information processing mode of the biology neuron and the quantum computing theory, we first propose a quantum neuron model which includes weighting, aggregating, activating, and prompting. Secondly, the quantum neural network model based on quantum neuron is constructed in which both the input and the output are real vectors and both the linked weight and the activation value are qubits. Using gradient descent algorithm, we also propose a super-linearly convergent learning algorithm of the quantum neural network. Finally, the availability of the approach is illustrated by two application examples of pattern recognition and function approximation. |