Quantum artificial intelligence integration for 6G networks

The amalgamation of quantum computing and artificial intelligence (AI) is poised to redefine the capabilities of future 6G networks. This paper presents a structured, system-level analysis of Quantum-enabled AI (QAI) for 6G, organized around a unifying technical framework that progresses from foundations to algorithms, integration, security and deployment. Specifically, we ask: How can quantum technologies be systematically integrated into AI-native 6G networks in a secure, scalable and practically deployable manner? To answer this, we first establish foundational concepts in quantum computing and hybrid quantum-classical architectures as the enabling substrate. Next, we classify quantum machine learning (QML) algorithms by learning paradigm, quantum subroutine, hardware model and ML pipeline stage, directly mapping each category to specific 6G network functions. Building on this algorithmic layer, we examine how QAI integrates into 6G AI pipelines across training, inference, sensing and federated learning. Beyond taxonomy, we provide a comparative tradeoff analysis of representative QML methods, highlighting their suitability, latency implications, hardware readiness and limitations across practical 6G communication scenarios. We then analyze the security implications of this integration, addressing new attack vectors, adversarial threats, hardware vulnerabilities and post-quantum cryptographic requirements that arise specifically at the quantum-classical interface. Finally, we identify the architectural, algorithmic and regulatory barriers that must be addressed before deployment and synthesize lessons learned and strategic recommendations for researchers, developers and policymakers. This layered, interdependent structure, from hardware substrate through algorithmic capability to secure deployment, provides a coherent roadmap for advancing trustworthy QAI ecosystems in 6G.