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 has not yet been met by a machine-learning decoder, nor by any decoder for promising resource-efficient codes such as the colour code. Here we introduce AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and colour codes at large scales under realistic noise. For the colour code, it is orders of magnitude faster than other high-accuracy decoders. For the surface code, we demonstrate real-time decoding faster than 1 microsecond per cycle up to distance 11 on current commercial accelerators with better accuracy than leading real-time decoders. 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.

翻译：容错量子计算要求错误率远低于物理量子比特所能达到的水平。量子纠错（QEC）能够弥合这一差距，但其实现依赖于解码器同时具备快速、准确和可扩展的特性。迄今为止，机器学习解码器或任何针对资源高效型编码（如色码）的解码器均未能满足这些综合要求。本文介绍AlphaQubit 2，这是一种神经网络解码器，能够在实际噪声条件下，针对大规模表面码和色码实现接近最优的逻辑错误率。对于色码，其解码速度比其他高精度解码器快数个数量级。对于表面码，我们在现有商用加速器上实现了距离11以内的实时解码，每周期解码速度快于1微秒，且精度优于主流实时解码器。这些结果支持了更广泛有前景的QEC编码的实际应用，并为容错量子计算所需规模下的高精度实时神经解码确立了可行路径。