Enhancing Quantum Machine Learning Performance via Transfer Learning Techniques

Abstract Quantum machine learning (QML) is a rapidly growing interdisciplinary field at the intersection of quantum computing and machine learning, aiming to harness the unique properties of quantum mechanics to address complex computational challenges and enhance algorithmic performance. Despite its potential, QML faces significant challenges such as computational complexity, data efficiency, and resource requirements. This paper introduces a novel transfer learning approach to improve the performance and efficiency of quantum models. By leveraging the knowledge from previously trained quantum models, our method reduces training time and enhances accuracy, making QML more practical for real-world applications. Experimental results validate the effectiveness of our approach, demonstrating substantial improvements in both accuracy and training time. PACS numbers: 03.67.Ac,03.67.Lx,03.67.Mn,03.67.Pp