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International Conference on Natural Computation (ICNC)

A Multi-layer Quantum Neural Networks Recognition System for Handwritten Digital Recognition

Daqi Zhu, Rushi Wu

Published Jan 1, 2007• 13 citations
Publisher

Abstract

In this paper, a handwritten digital recognition system based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers is proposed. The recognition system proposed consists of two layer sub-classifiers, namely first-layer QNN coarse classifier and second-layer QNN numeral pairs classifier. Handwritten digital recognition experiments are performed by using data from MNIST database. Experiment results indicate the proposed QNN recognition system achieves excellent performance in terms of recognition rates and recognition reliability, and at the same time show the superiority and potential of QNN in solving pattern recognition problems.

DOI
10.1109/icnc.2007.70

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The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
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A digital recognition paradigm integrating a hybrid quantum-traditional neural network is developed for numerical recognition, whereby quantum computing and conventional neural networks may collaborate. This model integrates parameterized quantum circuits within a convolutional framework to attain a combination of embedding and direct output. The input from the convolutional fully connected layer functions as the control parameter for the quantum layer and is subjected to a sequence of transformations for feature processing. This structure's potential is evidenced by digit recognition on the MNIST dataset for digital applications. The approach employs transfer learning, integrating convolutional and deep image feature extraction using hybrid networks, thereby mitigating the overfitting problem associated with a singular convolutional network on limited sample datasets. The findings demonstrate the model's efficacy, utility, and superior generalization capability. The recognition rates of the direct output hybrid network and the embedded hybrid network on the MNIST dataset increased by 0.0398 and 0.0211, respectively, in comparison to a convolutional network. Following transfer learning and feature fusion, the recognition rates of the hybrid model on small sample datasets exceed those of a singular network, achieving 83%, so validating the model's learning and generalization proficiency for digital applications.