Quantum-Aided Digital Twin Synchronization for Autonomous Vehicular Ecosystems over 6G-Enabled Edge Networks

QSync-Twin6G, a novel algorithmic paradigm, is proposed to address the emerging challenges in synchronizing autonomous vehicular ecosystems via quantum-assisted digital twin frameworks over 6G-enabled edge environments. This work conceptualizes and implements a hybrid quantum-twin model, where quantum entanglement principles are leveraged to achieve ultraprecise, low-latency synchronization across distributed vehicular digital replicas. QSync-Twin6G operates through a dual-layer synergy—Quantum State Propagation Module (QSPM) and Edge Twin Synchronization Engine (ETSE)— which collectively orchestrate entangled state coherence and seamless data state transition across vehicular nodes. The entanglement-assisted twin fidelity modeling ensures that vehicular events in physical space are instantaneously mirrored within their digital counterparts, minimizing update drift and eliminating the propagation lags inherent in classical digital twin approaches. Extensive simulations utilizing vehicular trace datasets and QuNetSim quantum communication modules validate the superiority of the proposed method in terms of synchronization precision, convergence rapidity, and energy-latency balance when benchmarked against existing AI-based and edge-driven synchronization frameworks. Performance indicators illustrate up to 81% reduction in update drift, 73% enhancement in convergence speed, and 62% energy savings across diverse vehicular scenarios. The proposed architecture signifies a leap toward cognitively self-aligning vehicular ecosystems, setting a new frontier in quantum-assisted edge computing and vehicular intelligence.