.png){kind=icon}
返回
Decoding surface code with a distributed neural network-based decoder
.png){kind=icon}
OA
AI
DOI：10.1007/s42484-020-00015-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There has been a rise in decoding quantum error correction codes with neural network-based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network-based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network-based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network-based decoder. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network-based decoders.
Keyword:
Quantum error correction
Quantum error detection
Surface code
Decoding
Artificial neural networks
期刊
Q
IF:
4.4
论文数:
440
被引数:
796
机构
引用论文
Genomic regions associated with performance in racing line of Quarter Horses与四分之一英里赛马品系性能相关的基因组区域
OAAI
OAAI分享收藏Hierarchical porous nano-MFI zeolite-pillared montmorillonite clay synthesized by recrystallization for hydrocracking of residual oil.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Rocket-grenade measurements of temperatures and winds in the mesosphere over Churchill, Canada加拿大丘吉尔上空中层温度和风的火箭手榴弹测量
PREAI
分享收藏BER-H2: a new anti-Ki-1 (CD30) monoclonal antibody directed at a formol- resistant epitopeBloodOAAI
BloodIF0
OAAI分享收藏
查看更多内容