用图神经网络实现通用高效量子纠错，速度超快精度翻倍。
Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correction
- 基于稳定子码的图结构设计机器学习解码器，时间复杂度线性。
- 在距离12的QLDPC码上逻辑错误率降至9.55e-5，提升18倍。
- 首次实现对任意稳定子码的实时通用解码，适合大规模量子计算。
扩展量子计算至实际应用需要可靠的量子错误纠正。尽管已有多种纠错码被提出，但整体纠错效率仍受限于解码算法。本文提出GraphQEC，一种基于稳定子码图结构的机器学习通用解码器，具有线性时间复杂度。该方法在所有测试码族中均表现出前所未有的准确性和效率，包括表面码、颜色码和量子低密度奇偶校验（QLDPC）码。例如，在距离为12的QLDPC码上，当物理错误率p=0.005时，GraphQEC实现逻辑错误率9.55×10⁻⁵，相比之前最优专用解码器的1.74×10⁻³提升18倍，同时保持每周期157μs的解码速度。本方法是首个适用于任意稳定子码的实时通用量子错误纠正方案。
原文摘要 · Abstract (English)
Scaling quantum computing to practical applications necessitates reliable quantum error correction. Although numerous correction codes have been proposed, the overall correction efficiency critically limited by the decode algorithms. We introduce GraphQEC, a code-agnostic decoder leveraging machine-learning on the graph structure of stabilizer codes with linear time complexity. GraphQEC demonstrates unprecedented accuracy and efficiency across all tested code families, including surface codes, color codes, and quantum low-density parity-check (QLDPC) codes. For instance, on a distance-12 QLDPC code, GraphQEC achieves a logical error rate of $9.55 \times 10^{-5}$, an 18-fold improvement over the previous best specialized decoder's $1.74 \times 10^{-3}$ under $p=0.005$ physical error rates, while maintaining $157μ$s/cycle decoding speed. Our approach represents the first universal solution for real-time quantum error correction across arbitrary stabilizer codes.
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