  This paper addresses the problem of qubit routing in first-generation and
other near-term quantum computers. In particular, it is asserted that the qubit
routing problem can be formulated as a reinforcement learning (RL) problem, and
that this is sufficient, in principle, to discover the optimal qubit routing
policy for any given quantum computer architecture. In order to achieve this,
it is necessary to alter the conventional RL framework to allow combinatorial
action space, and this represents a second contribution of this paper, which is
expected to find additional application, beyond the qubit routing problem
addressed herein. Numerical results are included demonstrating the advantage of
the RL-trained qubit routing policy over using a sorting network.
