  We propose a protocol to perform quantum reinforcement learning with quantum
technologies. At variance with recent results on quantum reinforcement learning
with superconducting circuits, in our current protocol coherent feedback during
the learning process is not required, enabling its implementation in a wide
variety of quantum systems. We consider diverse possible scenarios for an
agent, an environment, and a register that connects them, involving multiqubit
and multilevel systems, as well as open-system dynamics. We finally propose
possible implementations of this protocol in trapped ions and superconducting
circuits. The field of quantum reinforcement learning with quantum technologies
will enable enhanced quantum control, as well as more efficient machine
learning calculations.
