# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 4 Simulation Analysis
In Figures 6 and 7, the term \"quantum advantage\" refers to the measurable performance benefits exhibited by the quantum model compared to the classical model in addressing the 6G network optimization problem. These figures likely present a comparative analysis of key performance metrics, such as solution quality, convergence speed, and resource utilization. A notable crossover point may be observed where the quantum model begins to outperform the classical model, indicating the onset of quantum advantage. This advantage is particularly relevant to the specific context of 6G network optimization, showcasing practical benefits applicable to real-world scenarios. The data presented may highlight quantifiable improvements in aspects like faster convergence times and superior solution quality achieved by the quantum approach. Furthermore, the figures could illustrate how this advantage scales with increasing problem size or complexity, suggesting that larger or more intricate network configurations yield more pronounced benefits from the classical model. Figure 6: Execution time, speedup factor, average cost, and cost ratio vs. quantum advantage. quantum model. Importantly, while earlier analyses indicated higher costs associated with the quantum model, Figures 6 and 7 may reveal advantages in terms of resource efficiency and solution diversity that further underscore its potential superiority in this domain. Figure 8 displays a set of original sample images used as input for the 6G network simulation, providing a baseline for comparison with processed results. Figure 9 presents the results of quantum image processing in the 6G network context. These images appear significantly different from the original samples, likely due to the application of a quantum edge detection algorithm that highlights boundaries and significant features, resulting in a more transformative processing of the images. Figure 8: Sample images for 6G network simulation. Figure 7: Image processing execution time, speedup factor, average cost, and cost ratio vs. quantum advantage. Figure 9: Quantum image processing results in 6G network. Figure 10 shows the classical image processing results in the 6G network. These processed images strongly resemble the original samples, indicating that the classical approach, presumably using Gaussian blur, preserves more of the original image characteristics while reducing noise and detail. The distinct differences between quantum and classical processing results underscore their complementary strengths: quantum processing excels in rapid feature extraction and edge detection, while classical processing maintains higher overall image fidelity. This contrast suggests the potential advantages of integrating both approaches in a hybrid system for optimized image processing in 6G network applications. Figure 11 presents a PSNR comparison of quantum versus classical image processing in a 6G network context. The results show a stark contrast, with the quantum approach achieving a PSNR of 2, while the classical method attains a significantly higher PSNR of 55. This substantial difference in PSNR values indicates that the classical method vastly outperforms the quantum approach in preserving image quality and fidelity. The higher PSNR of the classical method aligns with earlier observations where classically processed images appeared more similar to the original samples. Despite its lower PSNR, the quantum approach may still offer advantages in specific applications such as rapid feature extraction or edge detection, where processing speed could be prioritized over image fidelity. This disparity highlights the potential for hybrid approaches that could leverage the strengths of both quantum and classical methods in 6G network image processing. The significant gap in PSNR performance underscores an important area for future research and development in quantum computing applications for image processing in 6G  
Figure 11: PSNR comparison: quantum vs. classical image processing in 6G network.