Two-Timescale Adaptive Decoding for Quantum Error Correction with Surface Codes

A real-time Quantum Error Correction (QEC) decoder should adapt to complex and evolving hardware noise while remaining sufficiently lightweight to meet stringent latency requirements. To decouple adaptivity from latency while enabling label-free runtime adaptation, we propose $\text{SF-QEC}$, a slow-fast two-timescale decoding framework. SF-QEC employs two structurally aligned recurrent-transformer models inspired by AlphaQubit 2: a neural decoder operates on the fast path for streaming decoding, while an adaptation model operates asynchronously on the slow path to analyze longer histories of detection events. The slow model extracts history-derived context and transfers it to the corresponding recurrent states of the fast decoder, augmenting its temporal representation without requiring runtime labels or placing the slow model on the latency-critical path. On QEC data collected from the IBM Phoenix superconducting quantum processor, the full SF-QEC framework further improves already-superior decoding performance compared to baselines, demonstrating the potential of two-timescale neural decoding under evolving hardware conditions.