  Quantum computing testbeds exhibit high-fidelity quantum control over small
collections of qubits, enabling performance of precise, repeatable operations
followed by measurements. Currently, these noisy intermediate-scale devices can
support a sufficient number of sequential operations prior to decoherence such
that near term algorithms can be performed with proximate accuracy (like
chemical accuracy for quantum chemistry). While the results of these algorithms
are imperfect, these imperfections can help bootstrap quantum computer testbed
development. Demonstrations of these algorithms over the past few years,
coupled with the idea that imperfect algorithm performance can be caused by
several dominant noise sources in the quantum processor, which can be measured
and calibrated during algorithm execution or in post-processing, has led to the
use of noise mitigation to improve computational results. Conversely, benchmark
algorithms coupled with noise mitigation can help diagnose the nature of noise,
whether systematic or purely random. Here, we outline the use of coherent noise
mitigation techniques as a characterization tool in trapped-ion testbeds. We
perform model-fitting of the noisy data to determine the noise source based on
realistic noise models and demonstrate that systematic noise amplification
coupled with error mitigation schemes provides useful data for noise model
deduction. Further, in order to connect lower level noise model details with
application specific performance of near term algorithms, we experimentally
construct the loss landscape of a variational algorithm under various injected
noise sources coupled with error mitigation techniques. This type of connection
enables application-aware hardware codesign, in which the most important noise
sources in specific applications, like quantum chemistry, become foci of
improvement in subsequent hardware generations.
