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.

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