  Today's Noisy Intermediate-Scale Quantum (NISQ) computers support only
limited sets of available quantum gates and restricted connectivity. Therefore,
quantum algorithms must be transpiled in order to become executable on a given
NISQ computer; transpilation is a complex and computationally heavy process.
Moreover, NISQ computers are affected by noise that changes over time, and
periodic calibration provides relevant error rates that should be considered
during transpilation. Variational algorithms, which form one main class of
computations on NISQ platforms, produce a number of similar yet not identical
quantum ``ansatz'' circuits. In this work, we present a transpilation
methodology optimized for variational algorithms under potentially changing
error rates. We divide transpilation into three steps: (1) noise-unaware and
computationally heavy pre-transpilation; (2) fast noise-aware matching; and (3)
fast decomposition followed by heuristic optimization. For a complete run of a
variational algorithm under constant error rates, only step (3) needs to be
executed for each new ansatz circuit. Step (2) is required only if the error
rates reported by calibration have changed significantly since the beginning of
the computation. The most expensive Step (1) is executed only once for the
whole run. This distribution is helpful for incremental, calibration-aware
transpilation when the variational algorithm adapts its own execution to
changing error rates. Experimental results on IBM's quantum computer show the
low latency and robust results obtained by calibration-aware transpilation.
