Quantum computing has the potential to revolutionize machine learning by offering exponential speed-up for specific algorithms. This study explores the theoretical and practical implications of using quantum computing to optimize machine learning models, such as in training neural networks. The findings provide insights into the possible improvements in computational efficiency, particularly for large datasets and complex models.