Quantum-Enhanced Federated Chaotic Transformer Framework for Ultra-Low Latency and Secure Multi-Site Telesurgery in 6G- Enabled Networks

Abstract The next generation of telesurgery demands quantum-resilient, ultra-low latency, and highly secure distributed AI frameworks capable of functioning across heterogeneous clinical sites. Building on prior federated learning-based telesurgery frameworks, this research introduces a Quantum-Enhanced Chaotic Gated Transformer Learning (Q-CGTL) architecture designed for real-time, multi-site surgical procedures in 6G-enabled smart hospital environments. The framework integrates (i) quantum random number generators (QRNGs) for entropy-rich key generation in the chaotic encryption layer, (ii) multi-head chaotic gated transformer (CGT) models optimized for multi-modal data streams (haptics, imaging, vitals), and (iii) 6G spectrum slicing with AI-driven latency control, reducing end-to-end feedback latency to under 1 ms. Additionally, a cross-institutional federated transfer learning module enables privacy-preserving model sharing without raw data exposure. Extensive evaluations using a synthetic Surgical Digital Twin dataset and the CIC-IDS-2023 cyber-attack dataset demonstrate a 99.4% intrusion detection accuracy, a 35% reduction in energy consumption, and near-zero packet loss under adversarial conditions. Statistical security assessments, including NIST entropy tests and post-quantum cryptographic analysis, confirm the robustness of the proposed system against quantum adversaries. This research represents a pivotal advancement towards autonomous, secure, and explainable telesurgery ecosystems capable of scaling across global clinical networks.