A novel optimizer named Superpositional Gradient Descent (SGD) has been introduced, which integrates classical training techniques with principles derived from quantum mechanics. This approach links gradient updates to the concept of quantum superposition, aiming to improve the training process of large language models. By harnessing these quantum-inspired methods, the optimizer seeks to enhance both convergence speed and generalization capabilities during model training. While the effectiveness of this optimizer is currently considered potential, it represents an innovative direction in the application of quantum principles to machine learning. The development aligns with ongoing efforts to optimize large-scale AI models, as reflected in recent research within the field. This advancement could contribute to more efficient and robust training methodologies for complex language models. Further empirical validation will be necessary to confirm the practical benefits of Superpositional Gradient Descent.