  Reliably executing quantum algorithms on noisy intermediate-scale quantum
(NISQ) devices is challenging, as they are severely constrained and prone to
errors. Efficient quantum circuit compilation techniques are therefore crucial
for overcoming their limitations and dealing with their high error rates. These
techniques consider the quantum hardware restrictions, such as the limited
qubit connectivity, and perform some transformations to the original circuit
that can be executed on a given quantum processor. Certain compilation methods
use error information based on calibration data to further improve the success
probability or the fidelity of the circuit to be run. However, it is uncertain
to what extent incorporating calibration information in the compilation process
can enhance the circuit performance. For instance, considering the most recent
error data provided by vendors after calibrating the processor might not be
functional enough as quantum systems are subject to drift, making the latest
calibration data obsolete within minutes. In this paper, we explore how
different usage of calibration data impacts the circuit fidelity, by using
several compilation techniques and quantum processors (IBM Perth and Brisbane).
To this aim, we implemented a framework that incorporates some of the
state-of-the-art noise-aware and non-noise-aware compilation techniques and
allows the user to perform fair comparisons under similar processor conditions.
Our experiments yield valuable insights into the effects of noise-aware
methodologies and the employment of calibration data. The main finding is that
pre-processing historical calibration data can improve fidelity when real-time
calibration data is not available due to factors such as cloud service latency
and waiting queues between compilation and execution on the quantum backend.
