Perspectives on quantum machine learning

Is quantum advantage the right goal for quantum machine learning?

This perspective article is a must-read for anyone working on or interested in quantum machine learning. In it, the authors argue that current research should not focus on attempting to "beat" existing classical machine learning approaches; rather, we should aim to better understand the fundamentals of how quantum learning may work. For example, what is the quantum analogue of a perceptron, and how can we quantum machine learning algorithms be related to better-understand classical learning theory?

In my opinion, unless someone can come up with a quantum version of the backpropagation algorithm, quantum neural networks will never be competitive with classical deep neural networks, because they will be too slow and expensive to train. 

Even avoiding this training issue (e.g. using kernel methods), the bottleneck of transferring data into and out of the quantum circuit will be huge in practical applications and may overwhelm any quantum speedups.

I am most excited about topological data analysis-based approaches because quantum TDA algorithms seem to avoid this data input bottleneck.