Variational quantum reinforcement learning for joint resource allocation of blockchain-based vehicular edge computing and Quantum Internet

Abstract

With the advances in artificial intelligence and communication technologies, vehicular edge computing (VEC), as a newly developed computing paradigm, is gaining more and more attention from both academia and industry. Complex demands and on-board applications need to be offloaded to edge servers for Quality of Experience (QoE). Nevertheless, the offloading process increases the risk of user privacy leakage, and the effectiveness of resource allocation algorithms is urgently desired in latency-sensitive tasks. To this end, we employ quantum key distribution (QKD) and blockchain to secure communication and computation, where key generation rate (KGR) associated with transmission and computation-aware is investigated for resource allocation problem. In consideration of the number of existing qubits and technical bottlenecks, we propose a tensor network preprocessing-based quantum deep reinforcement learning algorithm (TN-QDRL), which exploits amplitude encoding and the unique properties of quantum superposition and entanglement states to tackle the complex Markov decision process in a multi-dimensional state space. Additionally, we provide a search strategy for quantum state probabilistic transformations integrated with an improved Grover's algorithm. Simulation results indicate that our algorithm achieves a convergence speed that is 62.11% faster in high-dimensional real-world VEC scenarios and consumes 58.19% fewer quantum resources compared to other benchmarks.