QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction
Score
72提出噪声自适应预解码框架，并展示无需微调在真实硬件上的增益，信息增量明显。
实验设计严谨，覆盖合成分布偏移与真实硬件数据，对比基线清晰。
方案兼容现有解码器且无需目标域微调，对提升量子控制器性能具有直接落地价值。
量子纠错与神经解码结合正处于快速演进阶段，该成果紧随前沿。
来自 arXiv 预印本，使用谷歌 Willow 实际数据，但尚未经同行评议。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The paper validates neural pre-decoding on real hardware data with robustness to distribution shift without fine-tuning, demonstrating significant engineering value.
AI commentary
「What stands out most is that this paper doesn't try to replace global decoders but instead pre-decodes, and validates on real hardware data the value of adapting to noise drift. For those working on quantum control or error-correction system, it's a low-risk way to inject ML into existing quantum stacks.」
Quick overview
Fault-tolerant quantum computing relies on error correction to suppress noise, but classical decoder latency and strong, heterogeneous, nonstationary hardware noise pose major bottlenecks. QAdapt proposes a noise-adaptive neural pre-decoding framework that captures local spatiotemporal correlations in syndrome data, sequentially adapts to evolving noise while mitigating catastrophic forgetting, and forwards residual syndrome to a conventional global decoder. Across 110 synthetic out-of-distribution noise configurations and Google's Willow benchmark data, QAdapt reduces logical error rate and backend decoding latency without target-domain fine-tuning, offering a practical, decoder-compatible solution for robust quantum error correction.
- QAdapt uses a neural pre-decoder to capture local spatiotemporal correlations in syndrome data, naturally fitting surface-code error correction.
- With sequential adaptation and mitigation of catastrophic forgetting, QAdapt follows evolving hardware noise without target-domain fine-tuning.
- On 110 synthetic out-of-distribution noise configurations and Google Willow data, logical error rate drops by up to 5.79% and backend decoding latency by 9.32%.
Full text
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