  Quantum computing and machine learning have potential for symbiosis. However,
in addition to the hardware limitations from current devices, there are still
basic issues that must be addressed before quantum circuits can usefully
incorporate with current machine learning tasks. We report a new strategy for
such an integration in the context of attention models used for reinforcement
learning. Agents that implement attention mechanisms have successfully been
applied to certain cases of combinatorial routing problems by first encoding
nodes on a graph and then sequentially decoding nodes until a route is
selected. We demonstrate that simple quantum circuits can used in place of
classical attention head layers while maintaining performance. Our method
modifies the networks used in [1] by replacing key and query vectors for every
node with quantum states that are entangled before being measured. The
resulting hybrid classical-quantum agent is tested in the context of vehicle
routing problems where its performance is competitive with the original
classical approach. We regard our model as a prototype that can be scaled up
and as an avenue for further study on the role of quantum computing in
reinforcement learning.
