In the current era of noisy intermediate-scale quantum (NISQ) computers,
noisy qubits can result in biased results for early quantum algorithm
applications. This is a significant challenge for interpreting results from
quantum computer simulations for quantum chemistry, nuclear physics, high
energy physics, and other emerging scientific applications. An important class
of qubit errors are readout errors. The most basic method to correct readout
errors is matrix inversion, using a response matrix built from simple
operations to probe the rate of transitions from known initial quantum states
to readout outcomes. One challenge with inverting matrices with large
off-diagonal components is that the results are sensitive to statistical
fluctuations. This challenge is familiar to high energy physics, where
prior-independent regularized matrix inversion techniques (`unfolding') have
been developed for years to correct for acceptance and detector effects when
performing differential cross section measurements. We study various unfolding
methods in the context of universal gate-based quantum computers with the goal
of connecting the fields of quantum information science and high energy physics
and providing a reference for future work. The method known as iterative
Bayesian unfolding is shown to avoid pathologies from commonly used matrix
inversion and least squares methods.Comment: 13 pages, 16 figures; v2 has a typo fixed in Eq. 3 and a series of
minor modification
