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原文摘要

Fault-tolerant quantum computing relies on quantum error correction to protect logical information, requiring classical decoders that keep pace with syndrome generation. Heterogeneous, changing hardware noise and the distribution shift from simulation to experiment challenge the accuracy and robustness of fixed neural decoders. We present QAdapt, a noise-adaptive neural pre-decoding framework for surface-code quantum error correction. QAdapt learns local spatiotemporal correlations, continually adapts across noise environments, and passes the residual syndrome to a conventional global decoder. Online elastic weight consolidation limits forgetting as training moves between measurement, idle, CNOT, and Pauli-bias noise regimes. At distance nine, the HTNet pre-decoder reduces equivalent per-round logical error by 15.64% relative to Ising-Fast, with 28.75% fewer parameters and 9.07% less matching-backend time. Online EWC reduces average forgetting by 76.52% relative to sequential fine-tuning. With weights frozen, it reduces equivalent per-round logical error by 5.66% relative to continued base-environment training on a fixed valid domain of 331 unseen compound-noise conditions; whole-sequence failure probability falls by 2.54% over all 396 conditions. On native Willow records, the corresponding per-round reductions are 19.44%, 17.47%, and 17.95% at 10, 30, and 50 rounds, without target-domain fine-tuning. These results show how adaptive neural pre-decoding can improve logical accuracy, preserve performance across noise regimes, and reduce residual matching workload within an established decoding pipeline.