Bounding the systematic error in quantum error mitigation due to model
  violation
Quantum error mitigation is a promising route to achieving quantum utility,
and potentially quantum advantage in the near-term. Many state-of-the-art error
mitigation schemes use knowledge of the errors in the quantum processor, which
opens the question to what extent inaccuracy in the error model impacts the
performance of error mitigation. In this work, we develop a methodology to
efficiently compute upper bounds on the impact of error-model inaccuracy in
error mitigation. Our protocols require no additional experiments, and instead
rely on comparisons between the error model and the error-learning data from
which the model is generated. We demonstrate the efficacy of our methodology by
deploying it on an IBM Quantum superconducting qubit quantum processor, and
through numerical simulation of standard error models. We show that our
estimated upper bounds are typically close to the worst observed performance of
error mitigation on random circuits. Our methodology can also be understood as
an operationally meaningful metric to assess the quality of error models, and
we further extend our methodology to allow for comparison between error models.
Finally, contrary to what one might expect we show that observable error in
noisy layered circuits of sufficient depth is not always maximized by a
Clifford circuit, which may be of independent interest.
L. C. G. Govia S. Majumder S. V. Barron B. Mitchell A. Seif Y. Kim C. J. Wood E. J. Pritchett S. T. Merkel D. C. McKay