A Neural Decoder for Topological Codes

Giacomo Torlai, Roger G. Melko · 2016 · Phys. Rev. Lett. 119, 030501 (2017)

| 编号 QE249 | 主题 译码算法 | 链接 arXiv:1610.04238 |

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 an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the two dimensional toric code with phase-flip errors, with logical failure probability close to MWPM below the threshold.

本站词条在引用该文献时，会以 wikilink（sources/QE249）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。