Fault-tolerant quantum computation demands extremely low logical error rates, yet superconducting qubit arrays are subject to radiation-induced correlated noise arising from cosmic-ray muon-generated quasiparticles. The quasiparticle density is unknown and time-varying, resulting in a mismatch between the true noise statistics and the priors assumed by standard decoders, and consequently, degraded logical performance. We formalize joint noise sensing and decoding using syndrome measurements by modeling the QP density as a latent variable, which governs correlation in physical errors and syndrome measurements. Starting from a variational expectation--maximization approach, we derive an iterative algorithm that alternates between QP density estimation and syndrome-based decoding under the updated noise model. Simulations of surface-code and bivariate bicycle quantum memory under radiation-induced correlated noise demonstrate a measurable reduction in logical error probability relative to baseline decoding with a uniform prior. Beyond improved decoding performance, the inferred QP density provides diagnostic information relevant to device characterization, shielding, and chip design. These results indicate that integrating physical noise estimation into decoding can mitigate correlated noise effects and relax effective error-rate requirements for fault-tolerant quantum computation.

翻译：容错量子计算要求极低的逻辑错误率，但超导量子比特阵列会受到由宇宙射线缪子产生的准粒子所引发的辐射诱发热相关噪声的影响。准粒子密度未知且时变，导致真实噪声统计量与标准译码器所假设的先验信息之间存在失配，进而降低逻辑性能。我们通过将准粒子密度建模为潜变量，该变量控制物理错误和综合征测量中的相关性，从而形式化联合噪声感知与基于综合征测量的译码。从变分期望最大化方法出发，推导出一种迭代算法，该算法交替进行准粒子密度估计和基于更新噪声模型下的综合征译码。在辐射诱发热相关噪声下进行的表面码和双变量自行车量子存储器的仿真表明，与采用均匀先验的基线译码相比，逻辑错误概率有显著降低。除了改进的译码性能外，推断出的准粒子密度还提供了与器件表征、屏蔽和芯片设计相关的诊断信息。这些结果表明，将物理噪声估计整合到译码中可以减轻相关噪声效应，并放宽容错量子计算的有效错误率要求。