Machine-learning-assisted correction of correlated qubit errors in a topological code

P. Baireuther, T. E. O’Brien, B. Tarasinski, C. W. J. Beenakker · 2017 · Quantum 2, 48 (2018)

| 编号 QE251 | 主题 译码算法 | 链接 arXiv:1705.07855 |

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 show that a recurrent neural network can be trained, using only experimentally accessible data, to detect errors in the surface code with a performance above that of the established minimum-weight perfect matching decoder. The performance gain is achieved because the neural network decoder can detect correlations between bit-flip (X) and phase-flip (Z) errors. The machine learning algorithm adapts to the physical system, hence no noise model is needed. The long short-term memory layers maintain their performance over a large number of quantum error correction cycles.

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