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

arXiv:2604.16613v2 Announce Type: replace Abstract: 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 regime, unlocking circuit-level decoding of [[4,2,2]]-concatenated surface code memories with adaptive syndrome measurements -- a capability previously restricted to less accurate phenomenological decoders -- yielding 6.7x lower logical error rate and 4.5x lower decoding latency at a representative outer code distance of 10.

Abstract: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 regime, unlocking circuit-level decoding of [[4,2,2]]-concatenated surface code memories with adaptive syndrome measurements -- a capability previously restricted to less accurate phenomenological decoders -- yielding 6.7x lower logical error rate and 4.5x lower decoding latency at a representative outer code distance of 10.

Quantum Physics (quant-ph); Distributed, Parallel, and Cluster Computing (cs.DC)

arXiv:2604.16613 [quant-ph]

(or arXiv:2604.16613v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2604.16613 Focus to learn more arXiv-issued DOI via DataCite

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

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