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.

中文翻译：

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随机基准的抽样策略优化

随机基准测试 (RB) 是一种广泛使用的方法，用于估计在量子计算设备上实现的门的平均保真度。RB 估计的平均门保真度的随机误差取决于采样策略（即如何对要在协议中运行的序列进行采样）。采样策略由一组可配置参数（RB 配置）决定，其中包括 Clifford 长度（序列中独立 Clifford 门的数量列表）和每个 Clifford 长度的序列数量。RB 配置通常是启发式选择的，对其最佳配置的研究很少。因此，我们提出了一种完全优化 RB 配置的方法，以便在不增加序列总执行时间的情况下最小化估计保真度的置信区间。