Learning high-accuracy error decoding for quantum processors (arXiv: Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network)

Johannes Bausch, Andrew W. Senior, Francisco J. H. Heras, Thomas Edlich, Alex Davies, Michael Newman, Cody Jones, Kevin Satzinger, Murphy Yuezhen Niu, Sam Blackwell, George Holland, Dvir Kafri, Juan Atalaya, Craig Gidney, Demis Hassabis, Sergio Boixo, Hartmut Neven, Pushmeet Kohli · 2024 · Nature 635, 834-840 (DOI 10.1038/s41586-024-08148-8)

| 编号 QE262 | 主题 译码算法 | 链接 arXiv:2310.05900 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

We present a recurrent, transformer-based neural network which learns to decode the surface code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google’s Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on.

本站词条在引用该文献时，会以 wikilink（sources/QE262）链接到本页；右侧「反向链接」面板自动列出所有引用它的词条。