Scalable Neural Decoder for Topological Surface Codes.
Scalable Neural Decoder for Topological Surface Codes.
复制标题
用于拓扑表面代码的可扩展神经解码器。
DOI：
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发表时间：
2021
影响因子：
8.6
通讯作者：
S. Trebst
中科院分区：
文献类型：
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作者：
Kai Meinerz;Chae;S. Trebst
随着噪声中尺度量子(NISQ)设备的出现，实用的量子计算似乎已经实现。然而，为了超越原理证明计算，当前的处理架构将需要扩大到更大的量子电路，这将需要快速和可扩展的量子纠错算法。在这里，我们提出了一种基于神经网络的解码器，对于受去极化噪声和校正子测量误差影响的稳定器代码家族，它可以扩展到数万个量子比特(与其他最近受机器学习启发的解码器形成对比)，并且在很大范围的错误率(低至1%)下表现出比最先进的Union Find解码器更快的解码时间。关键的创新是通过在底层代码上移动预处理窗口来在小范围内自动解码错误征兆，这类似于模式识别方法中的卷积神经网络。我们表明，在实际应用中，这样的预处理步骤可以有效地将误码率降低高达2个数量级，并且通过检测相关效应，即使在存在测量误差的情况下，实际误差阈值也可以比联合查找或最小权重完美匹配等传统纠错算法的阈值高出15%。就地实施这种机器学习辅助的量子纠错将是将纠缠前沿推向NISQ地平线之外的决定性一步。
With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon.
影响因子：
6.4
作者：
Preskill, John
通讯作者：
Preskill, John