  Here we will give a perspective on new possible interplays between Machine
Learning and Quantum Physics, including also practical cases and applications.
We will explore the ways in which machine learning could benefit from new
quantum technologies and algorithms to find new ways to speed up their
computations by breakthroughs in physical hardware, as well as to improve
existing models or devise new learning schemes in the quantum domain. Moreover,
there are lots of experiments in quantum physics that do generate incredible
amounts of data and machine learning would be a great tool to analyze those and
make predictions, or even control the experiment itself. On top of that, data
visualization techniques and other schemes borrowed from machine learning can
be of great use to theoreticians to have better intuition on the structure of
complex manifolds or to make predictions on theoretical models. This new
research field, named as Quantum Machine Learning, is very rapidly growing
since it is expected to provide huge advantages over its classical counterpart
and deeper investigations are timely needed since they can be already tested on
the already commercially available quantum machines.
