  Quantum machine learning has emerged as an exciting and promising paradigm
inside quantum technologies. It may permit, on the one hand, to carry out more
efficient machine learning calculations by means of quantum devices, while, on
the other hand, to employ machine learning techniques to better control quantum
systems. Inside quantum machine learning, quantum reinforcement learning aims
at developing "intelligent" quantum agents that may interact with the outer
world and adapt to it, with the strategy of achieving some final goal. Another
paradigm inside quantum machine learning is that of quantum autoencoders, which
may allow one for employing fewer resources in a quantum device via a training
process. Moreover, the field of quantum biomimetics aims at establishing
analogies between biological and quantum systems, to look for previously
inadvertent connections that may enable useful applications. Two recent
examples are the concepts of quantum artificial life, as well as of quantum
memristors. In this Perspective, we give an overview of these topics,
describing the related research carried out by the scientific community.
