# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 4 Simulation and Analysis
### Simulation and Analysis
#### 4.2.3 Comparison of Noise Effects  
In this section, we compare the hybrid variational QNN model proposed in this paper with other quantum algorithms, specifically classical QNN image classification and QCNN image classification [49]. The circuit diagrams for the methods used by QNN and QCNN are depicted in Figure 9. For QNN, parameterized rotation gates and entanglement gates are applied. Given a specific number of layers, these gates transform the qubit states. The transformed qubit states are then measured by obtaining the expectation value of the Hamiltonian. These measurements are decoded into the appropriate output data format. Subsequently, the parameters are updated via the optimizer. QCNN convolutional circuits identify hidden states by applying multiple qubit gates between adjacent qubits. The quantum convolutional layer applies a filter to the input feature map, generating a new feature map composed of updated data. In cases where the system size is small, fully connected circuits are employed to predict classification results.  
Comprehensive model performance and training efficiency: Our model utilizes eight data qubits and incorporates five layers of variational blocks. In this classification task, the number of variables and optimization efficiency of our model are comparable to those of QNN and QCNN. Our model can achieve higher classification accuracy faster, but after  
Figure 8: RQNN, SQNN, and the model presented in this article analyze and compare the quaternary classification of the FashionMNIST dataset.  
Figure 9: (a) QNN diagram illustrating input, parameters, and linear entanglement structure. (b) Example of quantum convolutional layer for image classification.  
more training iterations, the classification accuracy of the three methods is close in a noise-free environment. We conduct quaternary classification tests for MNIST and FashionMNIST datasets in environments with noise values of 0, 0.03, and 0.1, respectively. The variable component sub-block is set to 5, and the test results are shown in Figure 10. As seen in the figure, in FashionMNIST image classification, the accuracy of our classification model can reach about 0.97 in a noise-free environment, which is comparable to other quantum classification algorithms. In the noise test with 0.03 slight noise, our model exhibits clear advantages and can still achieve a classification accuracy of more than 0.9. When the noise reaches 0.1, our model can still achieve an accuracy of more than 0.85, which is approximately 20% higher than that of the QNN model and about 17% higher than that of the QCNN model.  
Here, we demonstrate that our proposed hybrid variational QNN framework can achieve high accuracy in multivariate image classification. Our quantum neural network, combined with error mitigation, effectively reduces the impact of noise on experimental results. Experiments conducted on FashionMNIST datasets reveal that the classification performance of our model surpasses that of existing quantum classifiers.