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 $1.51 \times 10^{-11}$ with high confidence. Joint work with John Zhuoyang Ye, https://arxiv.org/abs/2602.04921

Bio: Jens Palsberg is a Professor and a former Department Chair of Computer Science at the University of California, Los Angeles (UCLA). His research interests span the areas of programming languages, software engineering, and quantum computing. He is the director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence, an associate editor of ACM Transactions on Quantum Computing, and a member of the ACM Council. In 2012, he received the ACM SIGPLAN Distinguished Service Award, and in 2023 he received the Excellence in Teaching Award from the UCLA Samueli Engineering School for his courses on quantum computing.