用学习方法动态选量子纠错码，大幅减少所需量子比特数。
Learning to Concatenate Quantum Codes
- 逐层分析噪声，智能选择最适配的纠错码
- 对强结构噪声可少用两数量级量子比特
- 适合早期容错量子计算，尤其噪声有规律时
量子纠错码的级联能以双指数方式降低逻辑错误率，但级联会改变噪声特性，导致难以选择最优码序列。本文通过估计每层后的有效噪声信道，自动选择下一阶段使用的码。针对具有明显结构的噪声，采用基于学习的小型非加性编码器；当噪声趋于均匀后，则切换至标准码。仿真显示，该分层自适应策略在达到目标逻辑错误率时，比单纯级联稳定子码节省大量量子比特——对强结构噪声最多减少两个数量级。因此，这种混合式学习策略为早期容错量子计算提供了有力工具。
原文摘要 · Abstract (English)
Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels. However, the noise structure shifts under concatenation, making it hard to choose an optimal code sequence. We automate this choice by estimating the effective noise channel after each level and selecting the next code accordingly. In particular, we use learning-based methods to tailor small, non-additive encoders when the noise exhibits sufficient structure, then switch to standard codes once the noise is nearly uniform. In simulations, this level-wise adaptation achieves a target logical error rate with far fewer qubits than concatenating stabilizer codes alone--reducing qubit counts by up to two orders of magnitude for strongly structured noise. Therefore, this hybrid, learning-based strategy offers a promising tool for early fault-tolerant quantum computing.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。