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Quantum Competitive Neural Network

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Abstract

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 2n, 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.

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Authors and Affiliations

  1. College of Information Engineering, East China Jiao Tong University, Nanchang, Jiangxi, 330013, P.R. China

    Rigui Zhou

  2. Department of Physics, Tsinghua University, Beijing, 100084, P.R. China

    Rigui Zhou

  3. Key Laboratory for Atomic and Molecular Nanosciences, Ministry of Education, Tsinghua University, Beijing, 100084, P.R. China

    Rigui Zhou

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  1. Rigui Zhou
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Correspondence to Rigui Zhou.

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Zhou, R. Quantum Competitive Neural Network. Int J Theor Phys 49, 110–119 (2010). https://doi.org/10.1007/s10773-009-0183-y

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  • DOI: https://doi.org/10.1007/s10773-009-0183-y