Quantum Associative Neural Network with Nonlinear Search Algorithm |
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Authors: | Rigui Zhou Huian Wang Qian Wu Yang Shi |
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Institution: | 1. College of Information Engineering, East China JiaoTong University, 330013, Nanchang, Jiangxi, PR China 2. Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, Hunan, China
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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( log22n -t ) = O( n - t )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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