Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

翻译：量子纠错是实现可靠量子计算的先决条件。为此，我们提出了一种基于递归Transformer的神经网络，该网络学习对领先的量子纠错码——表面码进行解码。在谷歌Sycamore量子处理器上距离为3和5的表面码真实数据测试中，我们的解码器性能优于最先进的算法解码器。在距离高达11的模拟数据（包含串扰、泄漏和模拟读出信号等真实噪声）测试中，该解码器保持优势，并在远超其训练周期（25个周期）的范围内保持高精度。我们的工作展示了机器学习通过直接学习数据超越人类设计算法的能力，凸显了机器学习作为量子计算机解码方案的强有力竞争者。