Daniel Kyungdeock Park

Academic Affiliation: Yonsei University

Title: Quantum machine learning: opportunities and challenges

Abstract:

Quantum computing has the potential to outperform any foreseeable classical computers for solving certain computational problems. With the growing demand for advanced computing power and methods in big data and artificial intelligence, quantum machine learning (QML) has emerged as one of the most exciting applications of quantum computing. In its early developments, QML gathered much attention mainly due to the quantum algorithm that solves the system of linear equations exponentially faster than its classical counterpart. However, this algorithm requires a fault-tolerant quantum computer and a quantum random access memory, which remain long-term prospect. Thus an important and challenging question is how noisy intermediate-scale quantum (NISQ) computers that are within reach can be utilized for QML. In this talk, I will first briefly introduce quantum machine learning. Then I will present several QML approaches that aim to utilize NISQ to the full extent and attain quantum advantages in the near future.