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.

翻译：电路级解码器是实现低开销容错量子计算的关键。然而，传统方法需要预先编译复杂的超图，这对一类新兴的自适应电路构成了重大瓶颈——这类电路在中途测量结果的基础上动态调整执行结构。为所有可能电路分支预编译超图将产生指数级内存开销，使得现有工具无法处理此类任务。为此，我们提出GreenPeas——基于C++/CUDA的高速即时编译解码超图工具链。通过将电路降维为时空错误传播图，我们展示了如何将Stim的后向追踪算法高效映射到大规模并行GPU架构上：对于含n个量子比特和l层门的电路，该算法可将O(nl)的计算负载分解到数千个并发线程中。在表面码和双正交自行车码这两种主流容错架构上的测试表明，我们的实现相比Stim基线实现了平均超10倍的加速。作为关键应用案例，我们证明该加速能力使自适应综合征测量电路的电路级解码成为可能，从而突破了此前仅能使用精度较低的现象学解码器的限制。我们将开源GreenPeas以支持未来自适应电路协议的研究。