AI 中文总结
研究针对颜色码实际应用受限问题，提出基于人工智能的预解码器，介绍其新型神经网络架构及简化训练数据方法，实验表明该预解码器能大幅提升逻辑故障率并减少运行时间，缩小与表面码性能差距。
AI 中文摘要
颜色码因其更简单的晶格手术协议和逻辑克利福德门的横向实现，有望成为通用容错量子计算中表面码的替代方案。然而，与表面码相比，其实际应用受限于较慢的解码算法、较差的逻辑故障率和阈值。尽管最近有人提出基于人工智能的逻辑翻转解码器来应对这些挑战，但目前尚无在大规模容错计算所需的时空并行块解码方案中实现此类解码器的明确框架。基于人工智能的预解码器因其局部性提供了一种可扩展的替代方案。通过对物理量子比特进行类空间校正和对稳定器测量进行类时间校正，预解码器自然地与并行块解码方案和晶格手术协议兼容。在这项工作中，我们为三角形颜色码引入了基于人工智能的预解码器。我们提出了一种用于其实现的新型神经网络架构，并开发了方法来简化由包含前馈操作的颜色码综合征提取电路生成的复杂训练数据。值得注意的是，我们发现随着码距增加，相对于原始Chromobius解码，逻辑故障率（LERs）和运行时间均有所改善。例如，在码距d = 31和物理错误率p = 0.3%时，我们的预解码器+Chromobius流水线将逻辑故障率提高了347倍，同时与单独的原始Chromobius解码相比，运行时间减少了7.33倍。这些结果表明，基于人工智能的预解码可以大大缩小颜色码和表面码之间的性能差距，使颜色码更接近实际的大规模容错量子计算。
英文摘要
Color codes are promising alternatives to surface codes for universal fault-tolerant quantum computing due to their simpler lattice-surgery protocols and the transversal implementation of logical Clifford gates. However, their practical deployment has been limited by slower decoding algorithms and worse logical failure rates and thresholds compared to surface codes. Although AI-based logical-flip decoders have recently been proposed to address these challenges, no clear framework currently exists for implementing such decoders within the parallel block-wise decoding schemes in both space and time required for large-scale fault-tolerant computation. AI-based pre-decoders offer a scalable alternative due to their local nature. By performing spacelike corrections on physical qubits and timelike corrections on stabilizer measurements, pre-decoders are naturally compatible with parallel block-wise decoding schemes and lattice-surgery protocols. In this work, we introduce AI-based pre-decoders for triangular color codes. We present a novel neural-network architecture for their implementation and develop methods to simplify the complex training data generated by color-code syndrome-extraction circuits containing feedforward operations. Remarkably, we find that both logical failure rates (LERs) and runtimes improve relative to raw Chromobius decoding as the code distance increases. For example, at code distance d=31 and physical error rate $p=0.3\%$, our pre-decoder + Chromobius pipeline improves the logical failure rate by a factor of 347x while reducing runtime by 7.33x compared to raw Chromobius decoding alone. These results demonstrate that AI-based pre-decoding can substantially narrow the performance gap between color codes and surface codes, bringing color codes closer to practical large-scale fault-tolerant quantum computation.
Comments16 pages, 12 figures, comments welcomed!
