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Sequence Input-Based Quantum-Inspired Neural Networks with Applications

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Abstract

To enhance the approximation and generalization ability of artificial neural networks (ANNs) by employing the principle of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state \(|1\rangle \) in the target qubit. Then a quantum-inspired neural networks (QINN) is designed by employing the quantum-inspired neurons to the hidden layer and the common neurons to the output layer. The algorithm of QINN is derived by employing the Levenberg–Marquardt algorithm. Simulation results of some benchmark problems show that, under a certain condition, the QINN is obviously superior to the classical ANN.

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Acknowledgments

We thank the two anonymous reviewers sincerely for their constructive comments and suggestions, which have tremendously improved the presentation and quality of this paper. This work was supported by the National Natural Science Foundation of China (Grant No. 61170132).

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Authors and Affiliations

  1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, China

    Panchi Li & Hong Xiao

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  1. Panchi Li
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  2. Hong Xiao
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Correspondence to Panchi Li.

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Li, P., Xiao, H. Sequence Input-Based Quantum-Inspired Neural Networks with Applications. Neural Process Lett 40, 143–168 (2014). https://doi.org/10.1007/s11063-013-9316-7

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