With the rapid proliferation of connected and autonomous vehicles (CAVs), there is an increasing necessity for real-time processing of tasks while maintaining energy efficiency. Task offloading has emerged as a crucial strategy to address the limitations of onboard resources. This review focuses on the latest advancements in quantum-inspired methodologies related to task scheduling for offloading in vehicular edge computing for road-based CAVs. It highlights three key areas: Quantum Reinforcement Learning (QRL), Quantum inspired Meta-heuristics algorithms, and a hybrid Digital Twin (DT) based learning model. The review encompasses studies that explore various edge/cloud architectures, caching strategies, and predictive modeling methods, such as LSTM sampling and DT. The findings indicate that quantum decision-making can significantly enhance offloading performance, achieving a 60% reduction in delay, 7–50% decrease in energy consumption, and 95% task success rates. Moreover, the inclusion of DT proves to be an effective method for real-time modeling of the entire vehicular system, enabling proactive adjustments to offloading choices according to fluctuating conditions and workloads. Overall, the review suggests that a resource-aware QRL aligned with a DT is the most effective approach, if not a hybrid model, for supporting large-scale ultra-reliable low-latency communication (URLLC) compliant vehicle systems.