SynQBench: Hardware-Grounded Synthetic Benchmarks for Neural Quantum Error Correction Decoder

Neural decoders for quantum error correction (QEC) rely heavily on synthetic data for both training and evaluation. However, the field currently lacks standardized, hardware-grounded benchmarks. Consequently, existing studies commonly depend on custom datasets with simplified noise assumptions, compromising the data quality and limiting the hardware relevance of resulting conclusions. In this work, we introduce SynQBench, a hardware-grounded synthetic benchmark for neural QEC decoders. SynQBench integrates device-dependent physical profiles and empirical calibrations into a circuit-aware noise model, enabling the scalable generation of faithful, labeled syndrome data. We validate the statistical fidelity of SynQBench against Google's Willow processor measurements, achieving a $6.9\times$ reduction in the mean absolute error of detector-event rates relative to the widely used SI1000 noise model, alongside substantially closer agreement with hardware spatial and temporal correlations. Furthermore, we demonstrate dual downstream value of SynQBench across the decoder development lifecycle: training on SynQBench reduces hardware logical error rates by $30.4$ to $42.0\%$ across four decoder architectures, while evaluating on SynQBench narrows the sim-to-real gap in logical error rate by $14.0\times$ across 16 trained decoders. Together, these results position SynQBench as a standardized, hardware-grounded foundation for reproducible neural QEC decoder development and assessment.