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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Pasam, Prudhvi Kiran
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Computer Science & IT Engineering

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Handwritten digit recognition using quantum convolution neural network Daniel, Ravuri; Prasad, Bode; Pasam, Prudhvi Kiran; Sudarsa, Dorababu; Sudhakar, Ambarapu; Rajanna, Bodapati Venkata
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp533-541

Abstract

The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
Co-Authors Daniel, Ravuri Prasad, Bode Rajanna, Bodapati Venkata Sudarsa, Dorababu Sudhakar, Ambarapu