  Neuromorphic computing approaches become increasingly important as we address
future needs for efficiently processing massive amounts of data. The unique
attributes of quantum materials can help address these needs by enabling new
energy-efficient device concepts that implement neuromorphic ideas at the
hardware level. In particular, strong correlations give rise to highly
non-linear responses, such as conductive phase transitions that can be
harnessed for short and long-term plasticity. Similarly, magnetization dynamics
are strongly non-linear and can be utilized for data classification. This paper
discusses select examples of these approaches, and provides a perspective for
the current opportunities and challenges for assembling quantum-material-based
devices for neuromorphic functionalities into larger emergent complex network
systems.
