# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 4 Simulation Analysis
However, the persistent edge of the classical model in solution quality, even at the highest simulated quantum advantage, underscores the need for continued advancements in quantum technology. These findings suggest that while quantum computing shows great promise for 6G network optimization, realizing its full potential may require further technological progress and careful consideration of the trade-offs between computational speed and solution quality in specific application contexts. Figure 6 illustrates the comparative performance of quantum and classical approaches to image processing in the context of 6G networks. The figure comprises four subplots, each highlighting a different aspect of the performance comparison. In the first subplot, which shows image processing execution time versus quantum advantage, the quantum model consistently demonstrates lower execution times compared to the classical model. As the quantum advantage increases, the quantum model's execution time further decreases, while the classical model's execution time remains constant. The second subplot displays the image processing speedup factor versus quantum advantage, where the speedup factor, calculated as the ratio of classical to quantum execution time, increases with growing quantum advantage. This indicates that the quantum model becomes progressively faster relative to the classical model as quantum technology improves. The third subplot, depicting average image processing cost versus quantum advantage, shows that the quantum model maintains lower average processing costs than the classical model across all quantum advantage levels. The quantum model's cost decreases as the quantum advantage increases, while the classical model's cost remains steady. Finally, the fourth subplot presents the image processing cost ratio versus quantum advantage, where the cost ratio, computed as the quantum cost divided by the classical cost, decreases as the quantum advantage grows. This trend suggests that the quantum model becomes increasingly cost-effective compared to the classical model with advancements in quantum technology. Overall, Figure 7 demonstrates the superior performance of the quantum model in both execution time and cost-effectiveness for the specific image processing tasks simulated in this 6G network context. Figure 7 illustrates the comparative performance metrics of quantum and classical approaches to image processing in 6G networks through four subplots, showing execution time, speedup factor, average cost, and cost ratio against quantum advantage. Figure 8 displays a set of sample images used as input for the 6G network simulation, providing a visual reference for the original data. In Figure 9, we see the results of quantum image processing in the 6G network context, where the images have undergone edge detection processing using the quantum approach. Figure 10 presents the classical image processing results in the 6G network, showing images that have been processed using Gaussian blur, representing the classical method. Figure 11 provides a PSNR (Peak Signal-to-Noise Ratio) comparison between quantum and classical image processing in the 6G network. This comparison reveals a significant difference in PSNR values between the two methods, with the classical method achieving much higher PSNR values of around 60, while the quantum method's PSNR values are considerably lower, at approximately 2. This substantial difference in PSNR values indicates that the classical method using Gaussian blur preserves more of the original image information than the quantum method employing edge detection. It is important to note that this difference is largely attributable to the inherent nature of the processing tasks, as edge detection fundamentally alters the image more drastically than Gaussian blur, naturally leading to lower PSNR values.