Learning high-accuracy error decoding for quantum processors.

ABSTRACT: Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems¹. Quantum error-correction codes² present a way to reach this goal by encoding logical information redundantly into many physical qubits. A key challenge in implementing such codes is accurately decoding noisy syndrome information extracted from redundancy checks to obtain the correct encoded logical information. Here we develop a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code³. Our decoder outperforms other state-of-the-art decoders on real-world data from Google's Sycamore quantum processor for distance-3 and distance-5 surface codes⁴

SUBMITTER: Bausch J 

PROVIDER: S-EPMC11602728 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature