论文标题
张量网络代码
Tensor-network codes
论文作者
论文摘要
受全息代码和张量 - 网络解码器的启发，我们引入了张量 - 网络稳定器代码，并带有天然张量 - 网络解码器。这些代码可以与任何几何形状相对应，但是，作为一种特殊情况，我们将全息代码推广到超出完美或完美的异形法构建的代码，我们给出了一个与都不相对的示例。使用张量 - 网络解码器，我们在去极化噪声下发现该代码的阈值为18.8％。我们还表明，对于全息代码，精确的张量 - 网络解码器（无键差截断）具有有效的效率，即使对于局部相关的噪声，复杂性也具有多项式的复杂性。
Inspired by holographic codes and tensor-network decoders, we introduce tensor-network stabilizer codes which come with a natural tensor-network decoder. These codes can correspond to any geometry, but, as a special case, we generalize holographic codes beyond those constructed from perfect or block-perfect isometries, and we give an example that corresponds to neither. Using the tensor-network decoder, we find a threshold of 18.8% for this code under depolarizing noise. We also show that for holographic codes the exact tensor-network decoder (with no bond-dimension truncation) is efficient with a complexity that is polynomial in the number of physical qubits, even for locally correlated noise.
