2021-08-26 QML Meetup: Hsin-Yuan (Robert) Huang, Power of data in quantum machine learning

2021-08-26 QML Meetup:
Hsin-Yuan (Robert) Huang,
Power of data in quantum machine learning

Abstract: Machine learning tasks where data is provided can be considerably different from commonly studied computational tasks. We show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We present a new quantum model based on the prediction error bounds and demonstrate a significant prediction advantage over some classical models on engineered data sets. The published work can be found at Nature Communication 12, 2631 (2021).