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Optics & Laser Technology

Improved error tolerance of programmable photonic integrated circuits for MNIST handwritten digit classification

Juan Zhang, Zhiyuan Wu, Yang Wang

Published Sep 22, 2023• 6 citations
Publisher
DOI
10.1016/j.optlastec.2023.110089

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Handwritten digit recognition (HWR) is a technology designed to enable mobile devices and computers to interpret handwritten inputs, encompassing both online and offline sources, such as images, scanned documents and digitised content. Various sectors, including insurance, banking, retail, healthcare and logistics, have increasingly adopted HWR. Despite the use of methods such as optical characteristic recognition (OCR), connectionist temporal classification (CTC) and machine learning (ML), their accuracy is compromised by interpretational limitations and significant error probabilities, leading to unreliable results. In order to address these shortcomings, this study advocates the collaborative use of deep ResNet and Inception models, combined with a variational quantum circuit (VQC) for HWR. The investigation utilises the MNIST dataset, which comprises 60,000 training set images and 10,000 testing set images of handwritten digits, each with a size of 28 × 28 pixels. Two selected samples, namely, Sample-1 (1, 0) and Sample-2 (3, 6), are drawn from this dataset. The hybrid approach of ResNet-Inception, which integrates a quantum encoding layer and VQC, is designed to enhance computational precision and digit recognition. ResNet improves accuracy through residual blocks with skip connections that mitigate vanishing and exploding gradients, while VQC enhances efficiency for high-velocity, large-scale datasets. The framework is evaluated using accuracy, precision, recall, and F1-score.

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In this paper, a Quantum Neural Network (QNN) has been proposed using the Projected Quantum Kernel feature for an image classification task. The QCNN consists of four dense layers; the first layer collects the quantum data as an input and the fourth layer produced the classification output. Moreover, a Quantum Generative Advisory Network (QGAN) has been developed using the patching technique for enhancing the number of samples in the image dataset. The proposed QNN and QGAN are constructed using quantum filters. The MNIST handwritten digit dataset was used to train and test the QNN model performance on image classification. A binary classification dataset was created from the MNIST handwritten digit database using digits 0 and 6. The QGAN generated 221 samples on digits 0 and 6 classes. The generated samples were added to the training dataset for the QNN model. The size of the Filtered MNIST handwritten dataset was extended from 13779 to 14000 samples. There are 12,000 images are split for training and 2,000 images for testing. The principal component analysis technique was used to reduce the dimension of the data. The QNN was trained on the enhanced dataset using a GPU environment. The testing accuracy of the QNN model was 98.65 percent; it is superior to the traditional neural network.