Abstract

Quantum machine learning extends the pool of hardware for machine learning by utilizing an entirely different class of computing device – the quantum computer. However, there appears to be much hype around quantum machine learning being “the best application for quantum computers”, and having “exponential speedups” against classical machine learning. Here, we present some of the latest quantum machine learning trends in the noisy intermediate-scale quantum (NISQ) era. These are motivated by the fact that attempting to prove quantum advantages can easily make us myopic – limiting our focus to only a handful of problems we can study in a complex field like ML. Therefore, we want to take notice of the periphery by encouraging researchers to ask important questions to lay the foundation of the field, to push quantum machine learning to realizing industrial-scale applications.