  Quantum Error Mitigation (QEM) enables the extraction of high-quality results
from the presently-available noisy quantum computers. In this approach, the
effect of the noise on observables of interest can be mitigated using multiple
measurements without additional hardware overhead. Unfortunately, current QEM
techniques are limited to weak noise or lack scalability. In this work, we
introduce a QEM method termed `Adaptive KIK' that adapts to the noise level of
the target device, and therefore, can handle moderate-to-strong noise. The
implementation of the method is experimentally simple -- it does not involve
any tomographic information or machine-learning stage, and the number of
different quantum circuits to be implemented is independent of the size of the
system. Furthermore, we have shown that it can be successfully integrated with
randomized compiling for handling both incoherent as well as coherent noise.
Our method handles spatially correlated and time-dependent noise which enables
to run shots over the scale of days or more despite the fact that noise and
calibrations change in time. Finally, we discuss and demonstrate why our
results suggest that gate calibration protocols should be revised when using
QEM. We demonstrate our findings in the IBM quantum computers and through
numerical simulations.
