Integrating Sustainable Big AI: Quantum Anonymous Semantic Broadcast

Semantic communication (SC) with native artificial intelligence (AI) is a context-centric framework that intelligently extracts task-specific semantics from source data and efficiently regenerates the intended meaning at the destination. Hence, this computing-intensive methodology enables goal-oriented communication by maintaining a high semantic quality of service with a low requirement for data transfer. Recently, the emergence of big-AI foundation models, such as the generative pre-trained transformer and diffusion models with zero-shot task generalization and native cross-modal learning capabilities, has brought a paradigm shift in designing AI-native frameworks for wireless networks. However, deploying big AI in wireless networks involves inherent challenges, such as large training parameters and computing requirements. To address these challenges, we use sustainability techniques, such as pruning and fine-tuning, to create sustainable (lightweight) models from big AI, which can reduce the resource consumption and environmental impact in computation-heavy SC systems while preserving or enhancing the task performance. Moreover, classical communication networks lack quantum-safe communication security and data privacy. In this article, we prototype a sustainable big AI-native quantum anonymous SC system. In this framework, we leverage big-AI models for semantic retrieval processing, that is, semantic extraction and recovery, and employ quantum anonymous communication protocols to broadcast semantics. We detail the underlying functionalities, sustainable practices, and potential challenges of integrating big AI into a quantum anonymous semantic broadcast system. We also formulate case studies demonstrating the sustainability and reliability of the envisioned framework. This work provides a sustainable and quantum-safe semantic communication framework by integrating big AI and quantum anonymous communication.