Quantum Machine Learning Classifier and Neural Network Transfer Learning

Abstract

This chapter explores quantum machine learning (QML) and neural network transfer learning. It begins by describing the potential of QML. The discussion then shifts to transfer learning, leveraging pre-trained neural models across diverse domains. A demonstration of advancements in both fields forms the core of the chapter, showcasing how QML classifiers can be used with classical neural networks for enhanced performance. To improve the accuracy of COVID-19 screening, ensemble method and sliding window mechanism measurements have been employed using computer vision on frequency domain spectrograms of audio files. Parallel with this, the accuracy of these measurements could be improved by quantum machine transfer learning. The chapter describes a case study where a hybrid approach demonstrated significant improvements in data processing accuracy, offering an understanding of practical applications. In conclusion, the authors present ideas on how the combination of QML and transfer learning could unfold new horizons in various fields with complex, large-scale datasets. The chapter concludes with predictions about the trajectory of these technologies, emphasizing their role in shaping the future of transfer learning. This combination of current research and visionary thinking inspires further exploration at the intersection of quantum computing machine learning and neural network transfer learning.