🤖 AI Summary
Fault-tolerant quantum computing demands real-time surface code decoders with low latency and high throughput to bridge the gap between room-temperature control systems and cryogenic hardware. This work presents the first implementation and validation of the Snowflake streaming decoder on a commercial FPGA under cryogenic conditions, demonstrating its practical feasibility. The authors introduce a locality-aware 2D sliced parallel architecture to enable scalable deployment across large code distances and integrate a lightweight confidence scoring mechanism with near-zero overhead. Experimental results show that the design achieves high-throughput decoding at small code distances, with extrapolated performance sufficient for larger distances. Critically, the added latency and resource consumption from the confidence scoring module are negligible, preserving the decoder’s efficiency while providing valuable reliability metrics.
📝 Abstract
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.