在室温和低温下使用置信度分数的流解码
Stream Decoding with Confidence Scores at Room and Cryogenic Temperatures
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中文总结 AI 辅助
本文在商用 FPGA 上实现了流式解码器 Snowflake，在室温和低温下验证其解码性能，引入置信度分数计算且开销可忽略，并提出利用其局部性的替代架构以应对大规模系统实现的挑战。
中文摘要 AI 辅助
在容错量子计算中，快速且准确的解码至关重要。Snowflake 是一种用于表面码的流式解码器。本文在商用 FPGA 上实现了 Snowflake，并在室温和低温下对其进行了验证。结果表明，对于小码距，该解码器具有高解码吞吐量，外推后仍能满足大码距的可接受范围。此外，我们在延迟和物理资源利用方面引入了特定解码器置信度分数的计算，其开销可忽略不计。我们注意到，实现大规模系统要么需要超出当前技术的大型 FPGA，要么需要通过高速总线连接的 FPGA 集群。因此，我们讨论了一种替代架构，该架构利用 Snowflake 的局部性，通过处理 3D 解码窗口的 2D 切片，并将 3D 结构的片段卸载到高速内存中。
英文摘要
In fault-tolerant quantum computing, fast and accurate decoding is crucial. Snowflake is a decoder for the surface code that runs in a streaming fashion. In this paper, we implement Snowflake on commercial FPGAs and validate them at room and cryogenic temperatures. Our results demonstrate high decoding throughput for small code distances that, when extrapolated, remains within acceptable limits for larger distances. Further, we incorporate the calculation of certain decoder confidence scores with negligible overhead both in terms of latency and physical resource utilisation. We note that implementing a large-scale system would require either a large FPGA beyond today's technology or clusters of FPGAs connected via a high-speed bus. Thus, we discuss an alternative architecture that exploits the locality of Snowflake by processing 2D slices of the 3D decoding window and offloading segments of the 3D structure to a high-speed memory.
