The standard method for benchmarking quantum error correction is randomized fault-injection testing. The state-of-the-art tool Stim is efficient for error-correction implementations with distances of up to 10, but scales poorly to larger distances for low physical error rates.

In this talk, I will present a scalable approach that combines stratified fault injection with extrapolation. The insight is that some of the fault space can be sampled efficiently, after which extrapolation is sufficient to complete the testing task. As a result, our tool scales to distance 17 for a physical error rate of 0.0005 with a two-hour time budget on a desktop. For this case, it estimated a logical error rate of $1.5 \times 10^{-11}$ with high confidence.