# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 4 Simulation and Analysis
### Simulation and Analysis
#### 4.2.2 FashionMNIST Accuracy and Anti-Noise Test  
In this experimental section, we conducted simulation experiments on the FashionMNIST dataset to explore the performance and noise resilience of our designed hybrid QNN denoising classification model in image classification tasks. FashionMNIST, a commonly used benchmark dataset for image classification, contains images of clothing and accessories. We selected images from four categories in FashionMNIST, with each category having 150 training samples, totaling 600 image samples, and 400 test samples. In the  
Figure 6: (**a**) Test accuracy of classification models using variable component sub-blocks of different layers on MNIST datasets. (**b**) Test loss of classification models using variable component sub-blocks of different layers on MNIST datasets.  
experiments, we consistently utilized five variational quantum blocks as the building units for the classifier. These variational quantum blocks manipulate the input quantum states through a series of quantum gates and parameterized operations. To simulate noise effects in real-world scenarios, we set two different levels of noise: 0.03 and 0.1. These noise values represent mild and significant random perturbations on pixel values in the images. The experimental results will be evaluated using metrics such as accuracy and loss values to gain insights into the performance and noise resilience of variational quantum blocks in image classification tasks on the FashionMNIST dataset.  
As depicted in the experimental results, the curves in Figure 7 illustrate the generalization ability of our model in image classification tasks. Even under the slight interference of noise with a value of 0.03, our model still achieves an accuracy of around 0.92. Under the more substantial interference of noise with a value of 0.1, the accuracy remains above 0.85, approaching 0.9, demonstrating commendable performance. Table 1 shows the confusion matrix of the classification results of the 100 th cycle without noise interference. These results indicate the effectiveness of our design and suggest further potential enhancements. Enhancing system robustness involves incorporating more intricate quantum transformation layers in the experimental framework, and employing optimization methods can elevate the system's classification performance. Error mitigation plays a significant role in mitigating noise effects on the images to a certain extent.  
We compare the model proposed in this paper with other recent quantum neural network classification algorithms in noisy environments. Here, we classify the test subset of the FashionMNIST dataset using SQNN [47] and RQNN [48]. This experiment still selected 400 test samples. The range of noise is set from 0.01 to 0.1. Figure 8 illustrates that the accuracy of our newly proposed classifier improves as the noise level increases,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **True Class1** & **True Class2** & **True Class3** & **True Class4** \\ \hline Prediction class1 & 95 & 3 & 5 & 0 \\ Prediction class2 & 1 & 91 & 2 & 4 \\ Prediction class3 & 2 & 3 & 90 & 1 \\ Prediction class4 & 2 & 3 & 3 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of classification results on FashionMNIST.  
Figure 7: **(a)** The training and testing accuracy and loss of the classification model using five-layer variable component sub-blocks on FashionMNIST datasets are carried out in the depolarization channel with a noise value of 0.03. **(b)** The training and testing accuracy and loss of the classification model using five-layer variable component sub-blocks on FashionMNIST datasets are carried out in the depolarization channel with a noise value of 0.1.  
and its accuracy under noise interference surpasses that of SQNN and RQNN. Our method effectively mitigates the impact of line noise on experimental results.