There has been much recent interest in near-term applications of quantum
computers. Variational quantum algorithms (VQA), wherein an optimization
algorithm implemented on a classical computer evaluates a parametrized quantum
circuit as an objective function, are a leading framework in this space.
In this paper, we analyze the iteration complexity of VQA, that is, the
number of steps VQA required until the iterates satisfy a surrogate measure of
optimality. We argue that although VQA procedures incorporate algorithms that
can, in the idealized case, be modeled as classic procedures in the
optimization literature, the particular nature of noise in near-term devices
invalidates the claim of applicability of off-the-shelf analyses of these
algorithms. Specifically, the form of the noise makes the evaluations of the
objective function via circuits biased, necessitating the perspective of
convergence analysis of variants of these classical optimization procedures,
wherein the evaluations exhibit systematic bias. We apply our reasoning to the
most often used procedures, including SPSA the parameter shift rule, which can
be seen as zeroth-order, or derivative-free, optimization algorithms with
biased function evaluations. We show that the asymptotic rate of convergence is
unaffected by the bias, but the level of bias contributes unfavorably to both
the constant therein, and the asymptotic distance to stationarity.Comment: 39 pages, 11 figure