Challenges and Opportunities of Quantum Machine Learning

謝明修 教授

鴻海研究院量子計算研究所所長

Abstract:

Quantum neural networks (QNNs) have been broadly used in various works with different levels of claimed benefits. One of my research interests in quantum machine learning is to understand the power and limitation of QNNs. In this talk, I will first compare the expressive power of QNNs with Boltzmann machines. Next, I will provide our results on the learnability of QNNs in terms of its trainability and generalization. Finally, I will provide a few applications of QNNs on machine learning tasks and ground state approximations.