学术
摘 要:
量子算法由于具有量子态的叠加性、相干性和纠缠性使得它可以解决一些经典NP问题,并且它具有许多传统算法所没有的优点。本文利用量子傅里叶变换提出了一个模式特征提取算法,它借助量子并行特性只需进行一次量子傅里叶变换就可以提取模式特征,所以它提取模式特征的速度比传统特征提取算法有了指数级的提高。利用该算法提取出来的特征可以进行模式识别或图像识别。本文通过理论推导证明了该算法的可行性,通过简单的模式图验证了该模式特征提取算法的有用性。[著者文摘]
文章出处:
《南京航空航天大学学报》-2008年40卷1期 -134-136页
文献标识码:
A
文章编号:
1005-2615（2008）01-0134-03
Pattern Feature Extraction Fourier Algorithm Based on Quantum Transform
Zhou Rigui, Yang Shuqun, Xu Xinwei, Cao Yongzhong, Ding Qiulin （College of Information Science and Technology, Nanjing University of Aeronautics Astronautics, Nanjing, 210016, China）
Abstract:
Quantum algorithm can solve some classical Non-polynomial problems in polynomial time and has many advantages of the superposition, coherence and entanglement of the quantum state. A quantum Fourier transform（QFT） is used. An algorithm for pattern feature extraction is presented to extract pattern features by only one operation of QFT due to quantum parallelism. Therefore,the proposed algorithm exhibits an exponential speed-up compared with the classical counterpart in the feature extraction. The features selected by the algorithm are used for the pattern recognition or the image recognition. Moreover, The feasibility of the algorithm is proved by theory deduction and the serviceability is validated by a simple pattern figure.[著者文摘]
Key words:
feature extraction; pattern recognition; quantum Fourier transform; image recognition
基金资助:
国防重大基础预研基金（S0500A001）资助项目;南京航空航天大学2006年度博士学位论文创新与创优基金（BCXJ06-10）资助项目.
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