Quantum Machine Learning from Theory to Data-Driven Implementations

By fusing the revolutionary machine learning technique with the special features of quantum computing, the quickly emerging subject of quantum machine learning aims to increase computation efficiency and smoothness in terms of both space and temporal complexities. A specific dataset pertaining to quantum computing has been evaluated and trained using a variety of machine learning techniques, such as logistic regression, random forests, and XGBoost. Recall, accuracy, and precision have all been computed and compared. Furthermore, various classifications of quantum machine learning algorithms and their challenges have been extensively discussed, exploring various opportunities and research areas in this field. The chapter’s conclusion highlights the significance of integrating the two cutting-edge, interdisciplinary fields of machine learning and quantum computing to further this research. This discovery adds to the literature on the intricacy and challenges of data processing in the potentially exploding field of quantum machine learning.