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Disturbance rejection in pattern recognition: a realization of quantum neural network

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Abstract

In the field of artificial intelligence, pattern recognition is widely used to extract the abstract information in those high dimensional inputs of image, voice, or video. However, the interpretability of pattern recognition still remains understudied. The incomplete features extracted from system input still limit the recognition performance. To reject the disturbance of feature incompleteness, an error compensation is realized into the pattern recognition model under a quantum computation framework. The quantum-based recognition system fulfills the information transmission from input to output with the transformation of quantum states. Then, a compensation for the quantum state is used to reject those intermediate errors in the pattern recognition task. The experiment results in this paper indicate an effectiveness of the proposed method, with which the compensated Quantum Neural Network obtains a better performance. The proposed method brings a more robust recognition system under unknown disturbances.

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Data Availability

The raw datasets used and analyzed during the current study are available in the open source MNIST repository http://yann.lecun.com/exdb/mnist/ and Cifar repository http://www.cs.toronto.edu/kriz/cifar.html.

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Acknowledgements

This work was partially financially supported by the Shanghai Cross-disciplinary Research Fund under Grants JYJC202214, and the National Natural Science Foundation of China under Grants 61533012, 91748120, and 52041502.

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Authors and Affiliations

  1. Joint Institute of UM-SJTU, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, Shanghai, China

    Xiaobo Hu & Jun Zhang

  2. Research Center of Intelligent Robotics, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, Shanghai, China

    Xiaobo Hu & Jianbo Su

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  1. Xiaobo Hu
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  2. Jianbo Su
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Correspondence to Jun Zhang.

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Hu, X., Su, J. & Zhang, J. Disturbance rejection in pattern recognition: a realization of quantum neural network. Quantum Inf Process 22, 409 (2023). https://doi.org/10.1007/s11128-023-04143-6

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