  Quantum matter, the research field studying phases of matter whose properties
are intrinsically quantum mechanical, draws from areas as diverse as hard
condensed matter physics, materials science, statistical mechanics, quantum
information, quantum gravity, and large-scale numerical simulations. Recently,
researchers interested quantum matter and strongly correlated quantum systems
have turned their attention to the algorithms underlying modern machine
learning with an eye on making progress in their fields. Here we provide a
short review on the recent development and adaptation of machine learning ideas
for the purpose advancing research in quantum matter, including ideas ranging
from algorithms that recognize conventional and topological states of matter in
synthetic an experimental data, to representations of quantum states in terms
of neural networks and their applications to the simulation and control of
quantum systems. We discuss the outlook for future developments in areas at the
intersection between machine learning and quantum many-body physics.
