Quantum Deep Deterministic Policy Gradient for Digital Twin-Enabled Semantic IoV Networks

Internet of Vehicles (IoV) networks are becoming more complex as they require real-time decision-making and efficient resource management. These demands make it difficult to maintain stable and reliable operations. The challenges are especially severe in dynamic and time-varying environments. To address these limitations, we propose a framework that integrates the quantum-based deep deterministic policy gradient (Q-DDPG) with digital twin networks (DTN) for distributed semantic optimization in dynamic IoV environments. The framework leverages quantum computing, such as superposition and entanglement, to enhance distributed semantic decisions. DTNs provide real-time modeling for efficient task offloading and adaptive resource allocation in decentralized IoV environments under varying conditions and uncertainties. The numerical results validate the robustness of the proposed approach, significantly reduce latency, and improve energy efficiency.