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International Conference on Optical Communications and Networks (ICOCN)

Simulation Study on Dataset Classification Based on Quantum Neural Networks

Jing Wang, Huan Chen, Junsen Lai

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/icocn70692.2026.11656162

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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.
The advancement of quantum computing has encouraged the use of Variational Quantum Neural Networks (VQNNs) as an alternative approach for data classification tasks. However, the implementation of VQNNs in the Noisy Intermediate-Scale Quantum (NISQ) era still faces major challenges due to quantum noise, which can significantly degrade model performance. This study aims to analyze the robustness of VQNNs against quantum noise in a binary classification task using a subset of the MNIST dataset. Image data are processed through normalization and dimensionality reduction using Principal Component Analysis (PCA), and subsequently encoded into quantum circuits using the angle encoding method. The VQNN model is designed with six qubits and a single variational layer, and evaluated under two conditions: an ideal quantum simulation (before noise, statevector) and a noisy quantum simulation (after noise). Quantum noise is simulated using Qiskit Aer, considering two types of noise, namely bit flip and depolarizing noise, across various probability levels. Experimental results show that under ideal conditions, the VQNN achieves a test accuracy of 85.2%. However, increasing noise probabilities lead to a gradual degradation in performance, approaching the random classification threshold at high noise levels. Bit flip noise causes a more severe performance decline compared to depolarizing noise. These findings confirm that quantum noise is a critical factor affecting the stability of VQNNs and highlight the importance of developing effective noise mitigation strategies for VQNN implementations in the NISQ era.
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.