Growing Dynamic Decoding Graph for High-Accuracy Quantum Error Correction

Quantum error correction requires high-fidelity error priors to improve the decoding accuracy. Existing approaches mainly focus on estimating the probabilities or weights of decoding priors, while largely neglecting their graph topology, which limits the error correction performance even when the edge weights are well estimated. This motivates us to ask whether the decoding graph itself can be automatically constructed from syndrome samples and a given decoder. To this end, we introduce the PrunerNet that applies physics-guided sparse self-attention over the hyperedges in the detector error model. Each hyperedge maintains a learnable embedding to determine its retention probability, and attention is restricted to pairs of hyperedges that share stabilizer check components, capturing physically meaningful correlations in the noise model. By growing dynamic decoding graphs with reinforcement learning and under the direct supervision of the logical error rates, we achieve high-precision decoding priors without human-engineered calibration, enabling high-fidelity quantum error correction. Benchmarking on error correction circuits of the surface code demonstrates the effectiveness of learning dynamic decoding graph structures compared with leading methods that optimize prior probabilities alone. Across correlated-error and randomly reordered-circuit benchmarks and relative to fixed-topology calibration, our method averagely reduces logical error rates by $3.8\%$ and $13.4\%$, respectively. We have also developed Auto-DEM, a modular compilation framework for detector error model, which enables joint optimization of the weights and topology of arbitrary decoding graphs, paving the way towards fault-tolerant quantum computing. All codes and evaluations are available at the https://anonymous.4open.science/r/Auto-DEM-ICLR-2027, and will be open-sourced upon publication.