GreenPeas: Unlocking adaptive quantum error correction with just-in-time decoding hypergraphs

Abbas B. Ziad et al. · 2026 · arXiv

| 编号 QE281 | 主题 译码算法 | 链接 arXiv:2604.16613 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

Circuit-level decoders are essential for the realisation of low-overhead fault-tolerant quantum computing. However, they rely on complex hypergraphs that are traditionally compiled ahead-of-time. This static approach introduces a significant bottleneck for an emerging class of adaptive circuits, where the structure is modified during execution based on mid-circuit measurement outcomes. Pre-compiling hypergraphs for all possible circuit branches would incur an exponential memory cost, rendering current tools impractical for these workloads. Hence, we introduce GreenPeas, a just-in-time compiler for decoding hypergraphs. By lowering the realised circuit to a space-time error propagation graph, GreenPeas decomposes Stim’s backtracking algorithm for error analysis into two sequentially dependent, internally parallelisable stages: (1) mapping physical errors to their corresponding equivalence classes, and (2) aggregating error probabilities within each class. Evaluated on surface and bivariate bicycle code memory circuits without user-annotated repeat blocks, GreenPeas achieves a geometric mean speedup of 13.2x over Stim using a high-end GPU. This speedup carries over to the adaptive re

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