Speaker
Description
Quantum error correcting (QEC) codes promise arbitrary suppression of logical errors as the size of the code increases. This tradeoff between experimental resources and error suppression makes the prospect of scalable quantum computing possible. Decoders play a key role in QEC protocols. They infer what class of logical error is most likely to have occurred during a computation based on information coming from a few sparse measurements performed on the system and a model of the underlying error mechanisms. Decoding is in principle a hard classical problem, and heuristics have been developed to decode efficiently in practice. In this talk I will present our latest advances in close-to-optimal decoding of stabilizer codes using device-level error models and tensor networks. In addition, I will present the results of our tensor network decoder applied to Google’s quantum processors. These results were recently used to support the first ever experimental demonstration of error suppression using the surface code.
