  We show that the method of iterative bayesian unfolding for mitigating
readout errors in quantum computers can be derived from an information
theoretic analysis. This inspires more flexible applications of this error
mitigation scheme. In particular, we distinguish between structural mitigation
and unstructural mitigation. Structural mitigation addresses nearly
deterministic quantum computation, where the computer is expected to output a
single or few outcome bitstrings. It is shown that the readout errors alone can
be corrected by few repetitions of the computation. In contrast, unstructural
mitigation is designed for quantum simulation, where the computer outputs
bitstrings broadly distributed. In this case, one is interested in mitigating
certain observables of interest. As most observables of interest are dependent
on few bits and not the whole bitstring, it is sufficient to mitigate the
marginal distributions over these dependent bits. As long as the cross-talk of
readout errors can be ignored, it is shown that the iterative bayesian
unfolding applied locally for these marginal distributions gives similar
results as mitigation using least squared errors. We illustrate our analysis
using the data of the preparation of the GHZ state in a 127-qubit quantum
computer.
