  Randomized benchmarking (RB) is a widely used method for estimating the
average fidelity of gates implemented on a quantum computing device. The
stochastic error of the average gate fidelity estimated by RB depends on the
sampling strategy (i.e., how to sample sequences to be run in the protocol).
The sampling strategy is determined by a set of configurable parameters (an RB
configuration) that includes Clifford lengths (a list of the number of
independent Clifford gates in a sequence) and the number of sequences for each
Clifford length. The RB configuration is often chosen heuristically and there
has been little research on its best configuration. Therefore, we propose a
method for fully optimizing an RB configuration so that the confidence interval
of the estimated fidelity is minimized while not increasing the total execution
time of sequences. By experiments on real devices, we demonstrate the efficacy
of the optimization method against heuristic selection in reducing the variance
of the estimated fidelity.
