AI 中文总结
针对非均匀偏置噪声下克利福德变形技术的计算低效问题，提出Chameleon编译器，通过近似方法优化变形，大幅降低计算开销并显著减少多种量子码的逻辑错误率。
AI 中文摘要
在具有偏置噪声的容错量子计算系统中，克利福德变形可在无需额外物理硬件开销（如额外量子比特、校验提取轮次或码距）的情况下大幅降低逻辑错误率（LER）。尽管Google Willow校准数据显示43%的量子比特呈现强X/Z偏置，但现有的感知校准变形技术仍不实用：（1）对6ⁿ种变形选择的全局搜索依赖于计算密集型模拟；（2）局部启发式方法的性能常逊于未变形的基准线。我们提出Chameleon，一种快速、高性能且与码无关的克利福德变形编译器。我们利用自身近似方法，基于LER的分析界解决变形问题；通过最小化该代理函数，Chameleon找到优化后的变形，以显著更低的计算开销实现经验性的LER降低。在评估中，使用从真实超导器件得到的校准模型，Chameleon显示其代理函数的改进与实际LER降低呈强相关性，在偏置最强的系统上平均秩相关系数为ρ=0.8，ρ=0.89-0.94；对于BB72码，它将经典计算时间从1.2天缩短至3.1分钟。相对于竞争基准，Chameleon对表面码实现了最大19%的LER降低（平均13%），对色码实现最大16%（平均7%），对二元自行车码实现最大10%（平均4%）；所有码族的最大增益均出现在偏置最强的系统上。
英文摘要
In fault-tolerant quantum computing systems with biased noise, Clifford deformation can substantially reduce the logical error rate (LER) without additional physical hardware overhead, such as extra qubits, syndrome extraction rounds, or code distance. Although Google Willow calibration data shows that $43\%$ of qubits exhibit strong $X/Z$ bias, existing calibration-aware deformation techniques remain impractical: (1) global searches over the $6^n$ deformation choices rely on computing-intensive simulations, and (2) local heuristics often underperform undeformed baselines. We present Chameleon, a fast, high-performance, and code-agnostic Clifford deformation compiler. We utilize our approximation to tackle a deformation problem based on an analytical bound on the LER. By minimizing this surrogate, Chameleon finds an optimized deformation that empirically reduces the LER with substantially lower computational overhead. In our evaluation, using calibration models derived from real superconducting devices, Chameleon demonstrates that improvements in our surrogate are strongly correlated with actual LER reductions, with an average rank correlation of $ρ=0.8$ and $ρ=0.89$-$0.94$ on the most strongly biased system. It also reduces classical computational time from $1.2$ days to $3.1$ minutes for the BB72 code. Chameleon achieves maximum LER reductions of $19\%$ ($13\%$ on average) for surface codes, $16\%$ ($7\%$) for color codes, and $10\%$ ($4\%$) for bivariate bicycle codes relative to competing baselines. The maximum gains for all code families are observed on the most strongly biased system.
Comments13 pages, 13 figures, 6 tables Github repository: https://github.com/WonJoon-Yun/Chameleon
