# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 1 Introduction  
In the rapidly evolving landscape of artificial intelligence and telecommunications [1], integrating deep learning with advanced network technologies like 6G presents transformative potential [2]. Deep learning, a subset of machine learning, leverages neural networks to enable machines to autonomously learn from vast amounts of data, making it a cornerstone technology for various applications, including image processing [3, 4]. As 6G networks promise unprecedented speed, connectivity, and adaptability, they also introduce dynamic and fluctuating network conditions that pose significant challenges for real-time data processing [5, 6, 7]. Furthermore, the rise of AI-generated content necessitates robust copyright detection mechanisms [8, 9] to safeguard intellectual property rights. These mechanisms utilize advanced algorithms to analyze digital content, identifying potential infringements and ensuring compliance with legal standards. This study aims to explore and compare the efficacy of quantum-enhanced and classical deep learning schemes for image processing [10, 11, 12] within these dynamic 6G network environments. Quantum computing [13, 14], with its principles of superposition and entanglement, offers a novel approach to handling complex computations and optimizations, potentially outperforming classical methodsin certain scenarios. Quantum Approximate Optimization Algorithm (QAOA) [14] and the Quantum neural networks (QNNs) [15; 16] represent the forefront of this technological advancement, providing new avenues for processing and analyzing high-dimensional data. Conversely, classical deep learning models, particularly convolutional neural networks (CNNs) [17; 18; 19], have demonstrated robust performance across a wide range of applications, from computer vision to natural language processing. These well-established models benefit from extensive research and optimization, making them a reliable benchmark for comparison. In this research, we simulate various network fluctuations, including bandwidth variations, latency changes, and node failures, to test the resilience and adaptability of both quantum and classical deep learning schemes. By evaluating key performance metrics such as processing speed, accuracy, and resource utilization under different network stress scenarios, we aim to provide a comprehensive analysis of each approach's strengths and limitations. The findings of this study will contribute to the ongoing discourse on the role of quantum computing in advancing 6G network capabilities and image processing techniques. By highlighting the challenges and opportunities inherent in both quantum and classical approaches, this research seeks to inform future developments in deep learning integration with next-generation network technologies. This research conducts a comprehensive comparative analysis of quantum-enhanced and classical deep learning schemes [20] for image processing within dynamic 6G network environments [21]. We seek to evaluate the performance, adaptability, and efficiency of both approaches under various network fluctuations that are characteristic of next-generation wireless networks. By simulating realistic 6G network conditions, including bandwidth variations, latency changes, and node failures [22], we aim to assess how quantum and classical models respond to these challenges. Our objective is to identify the strengths and limitations of each approach, particularly in maintaining image processing quality and computational efficiency amid network stress. This comparison will provide valuable insights into the potential advantages of quantum computing in handling complex network dynamics and the robustness of classical deep learning models in established network scenarios. Ultimately, this study aims to contribute to the ongoing discourse on the integration of quantum computing with 6G technologies and its implications for advanced image processing applications.