MDQEC-QAS：基于硬件感知VQC搜索和置信门控恢复的量子纠错元解码
MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery
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中文总结 AI 辅助
该研究提出用于量子纠错的统一元解码框架，跨多种码和噪声设置学习映射。通过硬件感知搜索选择VQC元解码器并与Meta-MLP比较，发现仅高准确率不够，置信门控回退可降低逻辑故障率，支持基于置信度的选择性恢复。
中文摘要 AI 辅助
我们提出了一个用于量子纠错的统一元解码框架，该框架可跨多个稳定器码和噪声设置学习从症候到恢复的映射，无需为每种配置单独设置解码器。基准测试包括五比特码、斯蒂恩码、平面3x3码和平面5x5码、四类噪声以及五种评估模式。我们将经典的经元多层感知器（Meta-MLP）教师训练的基线与通过硬件感知量子架构搜索在量子比特数、电路深度和纠缠拓扑方面选择的变分量子电路（VQC）元解码器进行比较。结果表明，仅高教师标签准确率在最具挑战性的平面5x5设置中是不够的，而置信门控回退可降低原始逻辑故障率。这些结果支持基于置信度的选择性恢复而非无条件的教师替换。
英文摘要
We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration. The benchmark includes FiveQubit, Steane, Planar3x3, and Planar5x5 codes, four noise families, and five evaluation regimes: interpolation, unseen-p transfer, unseen-noise transfer, few-shot unseen-code adaptation, and few-shot held-out-size adaptation. We compare a classical Meta-MLP teacher-trained baseline with variational quantum circuit (VQC) meta-decoders selected through hardware-aware quantum architecture search over qubit count, circuit depth, and entangling topology. The Meta-MLP achieves teacher-label accuracies of 0.9993, 0.9118, 0.9342, 0.6304, and 0.7548 across the five regimes, while the hardware-aware VQC achieves 0.9400, 0.8495, 0.8415, 0.5678, and 0.7143. However, logical-level evaluation shows that high teacher-label accuracy alone is insufficient in the most challenging Planar5x5 setting. During interpolation, the raw logical-failure ratios relative to the teacher are 12.08 and 25.91 for the Meta-MLP and VQC, respectively, whereas confidence-gated fallback reduces them to 1.71 and 1.11. These results support confidence-aware selective recovery rather than unconditional teacher replacement.
发表机构
- Department of Physics, Indian Institute of Technology (BHU)（印度理工学院（BHU）物理系）
- eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（纽约大学阿布扎克分校（NYUAD）工程学院eBRAIN实验室）
- Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD（纽约大学阿布扎克分校（NYUAD）量子与拓扑系统中心（CQTS））
机构由 AI 辅助整理，请以论文原文为准。
