Training a classifier with a superconducting quantum processor

Recent theoretical and experimental progress in quantum information suggests that there may be advantages in quantum-assisted heuristic algorithms for near-term devices, even at relatively shallow circuit depths. In the particular case of machine learning, in the light of increasing dataset sizes there is basis to hold the concern that traditional computational resources will become increasingly inefficient to growing challenges. In this talk, I will explore potential solutions to some of these problems by using quantum support to classical optimizers in solving classification problems.