Quantum Competitive Neural Network

被引：20
|
作者：
Zhou, Rigui [1 ,2 ,3 ]
机构：
[1] E China Jiao Tong Univ, Coll Informat Engn, Nanchang 330013, Jiangxi, Peoples R China
[2] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China
[3] Tsinghua Univ, Minist Educ, Key Lab Atom & Mol Nanosci, Beijing 100084, Peoples R China
来源：
INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS | 2010年 / 49卷 / 01期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
QCNN; Operators; Pattern storage; Pattern competition;
D O I：
10.1007/s10773-009-0183-y
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Quantum Neural Network (QNN) is a fledging science built upon the combination of classical neural network and quantum computing. After analyzing of traditional competitive neural network, this paper firstly presents a Quantum Competitive Neural Network (QCNN) that can recognize patterns and classify patterns via quantum competition. Contrasting to the conventional competitive neural network, the storage capacity or memory capacity of the QCNN is exponentially increased by a factor of 2 (n) , where n is the number of qubit. The QCNN has no weights, does not need to learn and update weights, which accelerates the learning process of the network. Besides, the case analysis validates the feasibility and validity of the QCNN in this paper.
引用
收藏
页码：110 / 119
页数：10
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