用Mamba模型加速量子纠错，速度提升且纠错能力更强。
Scalable Neural Decoders for Practical Real-Time Quantum Error Correction
- 采用状态空间模型Mamba替代Transformer，计算复杂度从O(d⁴)降至O(d²)
- 在真实硬件数据上性能相当，实时场景下错误阈值达0.0104，优于Transformer的0.0097
- 适合需要高速、高可靠性的量子计算纠错系统，尤其适用于大规模量子硬件
实时、可扩展且高精度的解码是实现容错量子计算机的关键。尽管基于Transformer的神经解码器如AlphaQubit已展现高准确率，但其核心注意力机制的计算复杂度随代码距离d呈O(d⁴)增长，导致解码速度不足以满足实际实时应用需求。本文提出并评估了一种基于Mamba的解码器，该状态空间模型具有O(d²)复杂度。在使用Sycamore硬件数据的内存实验中，Mamba解码器性能与Transformer相当，且效率更高。关键的是，在模拟实时场景中考虑解码引入噪声后，Mamba解码器显著优于Transformer，错误阈值达到0.0104，高于Transformer的0.0097。结果表明，Mamba解码器在速度与准确性之间实现了良好平衡，是实现可扩展实时量子纠错的有前途架构。
原文摘要 · Abstract (English)
Real-time, scalable, and accurate decoding is a critical component for realizing a fault-tolerant quantum computer. While Transformer-based neural decoders such as \textit{AlphaQubit} have demonstrated high accuracy, the computational complexity of their core attention mechanism, which scales as $\mathcal{O}(d^4)$ with code distance $d$, results in decoding speeds insufficient for practical real-time applications. In this work, we introduce and evaluate a \textit{Mamba}-based decoder, a state-space model with $\mathcal{O}(d^2)$ complexity. In memory experiments using Sycamore hardware data, our Mamba decoder matches the performance of its Transformer-based counterpart, providing that its superior efficiency does not come at the cost of performance. Crucially, in simulated real-time scenarios that account for decoder-induced noise, the Mamba decoder significantly outperforms the Transformer, exhibiting a higher error threshold of $0.0104$ compared to $0.0097$. These results demonstrate that Mamba decoders offer a compelling balance between speed and accuracy, making them a promising architecture for scalable, real-time quantum error correction.
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