---
title: "QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.28422v1"
permalink: "https://feed.astralor.com/items/itm_7befa0d32fc2988011a0bdf6"
published: "2026-07-30T16:02:00.000Z"
score: 72
channel: "tech"
tags: ["QAdapt","Willow","Neural Pre-Decoding","Surface Code","Google","Quantum Error Correction"]
---

# QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction

## 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.

## At a glance

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.

## Key points

- 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

Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale. In practice, however, performance is constrained not only by physical noise but also by the latency of classical decoders processing rapidly generated syndrome data. This challenge is exacerbated by hardware noise that is strong, heterogeneous, and nonstationary, as well as by the simulation-to-hardware distribution shift that can substantially degrade fixed neural decoders. We present QAdapt, a noise-adaptive neural pre-decoding framework for surface-code quantum error correction. QAdapt captures local spatiotemporal correlations in syndrome data, sequentially adapts to evolving noise conditions while mitigating catastrophic forgetting, and forwards the residual syndrome to a conventional global decoder. Across 110 synthetic out-of-distribution noise configurations for rotated surface-code memory circuits, QAdapt consistently reduces the logical error rate relative to the neural pre-decoding baseline. On Google's Willow benchmark data, without target-domain fine-tuning, it achieves reductions of up to 5.79 percent in logical error rate and 9.32 percent in backend decoding latency on the residual syndrome. These results demonstrate that QAdapt provides a practical and decoder-compatible approach to improving the robustness and backend decoding efficiency of quantum error correction under evolving hardware noise.

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