量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析

解光軍; 李斌; 莊鎮(zhèn)泉

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量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析

解光軍, 李斌, 莊鎮(zhèn)泉

文章導航 > 電子與信息學報 > 2003 > 25(5): 606-611

解光軍, 李斌, 莊鎮(zhèn)泉. 量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析[J]. 電子與信息學報, 2003, 25(5): 606-611.

引用本文:

解光軍, 李斌, 莊鎮(zhèn)泉. 量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析[J]. 電子與信息學報, 2003, 25(5): 606-611.

Xie Guangjun, Li Bin, Zhuang Zhenquan. Theoretical analysis of the implementation of neural computation in quantum systems[J]. Journal of Electronics & Information Technology, 2003, 25(5): 606-611.

Citation:

Xie Guangjun, Li Bin, Zhuang Zhenquan. Theoretical analysis of the implementation of neural computation in quantum systems[J]. Journal of Electronics & Information Technology, 2003, 25(5): 606-611.

解光軍, 李斌, 莊鎮(zhèn)泉. 量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析[J]. 電子與信息學報, 2003, 25(5): 606-611.

引用本文:

解光軍, 李斌, 莊鎮(zhèn)泉. 量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析[J]. 電子與信息學報, 2003, 25(5): 606-611.

Xie Guangjun, Li Bin, Zhuang Zhenquan. Theoretical analysis of the implementation of neural computation in quantum systems[J]. Journal of Electronics & Information Technology, 2003, 25(5): 606-611.

Citation:

Xie Guangjun, Li Bin, Zhuang Zhenquan. Theoretical analysis of the implementation of neural computation in quantum systems[J]. Journal of Electronics & Information Technology, 2003, 25(5): 606-611.

量子系統(tǒng)實現(xiàn)神經(jīng)計算的理論分析

解光軍^{①②;李斌},
李斌,
莊鎮(zhèn)泉

計量
- 文章訪問數(shù): 2499
- HTML全文瀏覽量: 100
- PDF下載量: 636
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2001-09-03
- 修回日期: 2002-06-24
- 刊出日期: 2003-05-19

Theoretical analysis of the implementation of neural computation in quantum systems

Xie Guangjun^{①②;李斌},
Li Bin,
Zhuang Zhenquan

摘要

摘要: 該文通過量子系統(tǒng)的演化與人腦信息處理過程的比較研究，發(fā)現(xiàn)量子理論與神經(jīng)網(wǎng)絡理論中存在有許多相對應的數(shù)學表述，利用其中有關(guān)的公式提出一個廣義的基于量子系統(tǒng)的Hebb學習規(guī)則，并結(jié)合量子點系統(tǒng)設計了一個神經(jīng)計算的理論模型。
- 量子信息處理; 神經(jīng)計算; 量子系統(tǒng)
Abstract: Through the comparison between quantum systems and information processing in human brain it can be found that there are many similar mathematical expressions both in quantum theory and neural networks, with some formula a generalized Hebb learning algorithm is proposed based on quantum system, and its convergence performance is also certified. Finally, a quantum neural computational model in a quantum dot system is designed, after analysing it results in that this model can realize the learning of simple logic.

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參考文獻(1)

R. Penrose.[J].The Enperors New Mind, New York, Oxford University Press.1989,:-[2]S.R. Hameroff, Quantum coherence in microtubules: An intra-neuronal substrate for emergent consciousness? J. of Consciousness Studies, 1994, 1, 91-118.[3]M. Perus, Neuro-quantum parallelism in brain-mind and computers, Informatica, 1996, 20, 173-183.[4]曾謹言,量子力學,第三版,北京,科學出版社,2000,23-56,270-295.[5]H. Haken, Synergetic Computers for Pattern Recognition, and Their Control by Attention Parameter, In Neurocomputers and Attention II, Connectionism and Ncurocomputers, V. I. Kryukov,A. Holden (Eds.), UK, Manchester University Press, 1991, 551-556.[6]E. Fahri, S. Gutman, Analog analogue of a digital quantum computation, Phys. Rev. A., 1998,57, 2403-2407.[7]J.C. Lusth, B. Dixon, A characterization of important algorithms for quantum-dot cellular automata.[J]. Information Science.1999,113:193-[8]E. Behrman, et al., Simulation of quantum neural networks.[J]. Information Science.2000,128:257-[9]R.P. Feynman, A. R. Hibbs, Quantum Mechanics and Path Integrals, New York, McGraw-Hill,1965, 149-236.

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