**Abstract:** This research proposes a novel methodology, Dynamic Quantum Metrological Calibration (GQMC), for significantly enhancing the precision of the gravitational constant (G) prediction. Leveraging advances in trapped ion quantum computing and high-throughput Bayesian optimization, GQMC dynamically calibrates measurement systems in real-time, mitigating systematic errors and achieving a projected 10x improvement in G accuracy compared to current state-of-the-art experimental methods. GQMC centers around a feedback loop that fuses quantum metrology with a reinforcement learning agent optimizing the experimental setup.

**Abstract:** This paper presents HyDAC-ART, a novel system for achieving highly accurate dynamic alignment compensation in wireless electric vehicle (EV) charging scenarios. Addressing the limitations of current systems reliant on fixed alignment tolerances, HyDAC-ART leverages a sensor fusion approach combining high-frequency radar, LiDAR, and inertial measurement units (IMUs) to create a robust, real-time model of relative vehicle positioning. This model is then fed into an Adaptive Resonance Theory (ART) neural network, allowing for rapid learning and adaptation of charging parameters to maintain optimal energy transfer efficiency despite continuous vehicle movement.