神经网络解码器实现量子纠错码实时高精度解码，突破速度与规模瓶颈。
A scalable and real-time neural decoder for topological quantum codes
- 基于神经网络设计可扩展的实时解码器，支持表面码与彩色码。
- 彩色码解码速度比现有方法快数量级，每周期低于1微秒。
- 首次实现彩色码距离9的实时解码，适合未来容错量子计算部署。
容错量子计算需要远低于物理量子比特误差率的性能。量子错误纠正（QEC）填补这一差距，但依赖解码器同时具备高速、高准确性和可扩展性。当前机器学习解码器及资源高效的色码等先进编码方案均难以满足此三重需求。本文提出AlphaQubit 2，一种神经网络解码器，在真实噪声下对表面码和色码均实现近最优逻辑错误率。在色码上，其速度比其他高精度解码器快数个数量级。我们展示了在商用加速器上每周期解码时间小于1微秒：表面码距离11时，精度优于现有实时解码器；首次实现色码距离9的实时解码。这些结果推动了更广泛有前景的QEC编码的实际应用，并为实现大规模容错量子计算所需的高精度、实时神经解码提供了可信路径。
原文摘要 · Abstract (English)
Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of requirements remains unmet by a machine-learning decoder, nor by any decoder for promising resource-efficient codes such as the color code. Here we introduce AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and color codes at scale under realistic noise. For the color code, it is orders of magnitude faster than other high-accuracy decoders. We demonstrate real-time decoding faster than 1μs per cycle on commercial accelerators: for the surface code to distance 11, with better accuracy than leading real-time decoders; and the first real-time decoding of the color code to distance 9. These results support the practical application of a wider class of promising QEC codes, and establish a credible path towards high-accuracy, real-time neural decoding at the scales required for fault-tolerant quantum computation.
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