Quantum machine learning, which uses the parallel computability of quantum information, has been proven to provide speed improvement. It became a subcategory of quantum information/computing ever since. However, there are still many discussions on aspects of quantum machine learning and speed improvement, and many research visions are being developed using keywords like NISQ. This presentation's goal is to provide a picture of the up-to-date issues/results of quantum machine learning, and discuss the destination of future research.

JB holds the PhD in quantum machine learning and has been continuing his career to conducting the research on fundamental quantum information theory, quantum algorithms, etc. Currently JB serves as the Director of Quantum Computing Lab. at the Electronics and Telecommunications Research Institute (ETRI).