GreenPeas: Unlocking Adaptive Quantum Error Correction with Just-in-Time Decoding Hypergraphs
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arXiv
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| Format: | Preprint |
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2026
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| author | Ziad, Abbas B. Xu, Jubo Fan, Hongxiang |
| author_facet | Ziad, Abbas B. Xu, Jubo Fan, Hongxiang |
| contents | 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 C++/CUDA toolchain for the high-speed, just-in-time compilation of decoding hypergraphs. By lowering the circuit to a space-time error propagation graph, we show how Stim's backtracking algorithm can be mapped efficiently onto massively parallel GPU architectures, decomposing the O(nl) workload for a circuit with n qubits and l gate layers across thousands of concurrent threads. Our implementation achieves a greater than 10x average speedup over the Stim baseline across two of the leading fault-tolerant architectures: the surface and bivariate bicycle codes. As a key use case, we demonstrate that this speedup enables circuit-level decoding of adaptive syndrome measurement circuits, unlocking a regime previously restricted to less accurate phenomenological decoders. We aim to open-source GreenPeas to support the research of future adaptive circuit protocols. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_16613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GreenPeas: Unlocking Adaptive Quantum Error Correction with Just-in-Time Decoding Hypergraphs Ziad, Abbas B. Xu, Jubo Fan, Hongxiang Quantum Physics Distributed, Parallel, and Cluster Computing 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 C++/CUDA toolchain for the high-speed, just-in-time compilation of decoding hypergraphs. By lowering the circuit to a space-time error propagation graph, we show how Stim's backtracking algorithm can be mapped efficiently onto massively parallel GPU architectures, decomposing the O(nl) workload for a circuit with n qubits and l gate layers across thousands of concurrent threads. Our implementation achieves a greater than 10x average speedup over the Stim baseline across two of the leading fault-tolerant architectures: the surface and bivariate bicycle codes. As a key use case, we demonstrate that this speedup enables circuit-level decoding of adaptive syndrome measurement circuits, unlocking a regime previously restricted to less accurate phenomenological decoders. We aim to open-source GreenPeas to support the research of future adaptive circuit protocols. |
| title | GreenPeas: Unlocking Adaptive Quantum Error Correction with Just-in-Time Decoding Hypergraphs |
| topic | Quantum Physics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.16613 |