The study introduces innovative methods that combine quantum computers and deep learning to combat the considerable optimization challenges in supply chain management, particularly the Vehicle Routing Problem of Time Windows (VRPTW). Due to the complex combination of problems, traditional algorithms are difficult to solve large instances. The study uses the inherent parallelism of quantum systems and the predictive capabilities of deep learning models to efficiently study solution spaces. Deep neural networks are used to learn from historical data and predict the optimal route, whereas quantum algorithms improve these predictions, leading to shortening right-wing times. Experimental results show a significant improvement in solution quality compared to calculation time, compared to traditional heuristics and metasia-algorithms, to reduce calculations by approximately 60%. This quantum system can be implemented in both current and future quantum hardware. Reduce computing time and improved efficiency in route optimization. This study contributes to the ambitious field of quantum supplementary deep learning with the potential to radically transform supply chain and logistical optimization.