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Quantum Associative Neural Network with Nonlinear Search Algorithm

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Abstract

Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to \(O( {\log_{2}}^{2^{n -t}} ) = O( n - t )\) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers’ counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

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Authors and Affiliations

  1. College of Information Engineering, East China JiaoTong University, 330013, Nanchang, Jiangxi, PR China

    Rigui Zhou, Huian Wang, Qian Wu & Yang Shi

  2. Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, Hunan, China

    Rigui Zhou

Authors
  1. Rigui Zhou
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  2. Huian Wang
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  3. Qian Wu
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  4. Yang Shi
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Correspondence to Huian Wang.

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Zhou, R., Wang, H., Wu, Q. et al. Quantum Associative Neural Network with Nonlinear Search Algorithm. Int J Theor Phys 51, 705–723 (2012). https://doi.org/10.1007/s10773-011-0950-4

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  • DOI: https://doi.org/10.1007/s10773-011-0950-4

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