用张量网络精确学习量子噪声

中文摘要 AI 辅助

精确的噪声模型对于高性能量子纠错至关重要，但表征量子设备的噪声通常需要专门的实验。我们提出一种变分框架，可直接从纠错存储实验期间收集的量子纠错症候群与逻辑可观测量数据中学习噪声模型。故障事件概率被视为变分参数，通过梯度下降优化以最小化解码器预测与实验逻辑可观测量结果之间的二元交叉熵。我们证明该目标是有原则的而非特设的：足够表达力的噪声拟设可达到信息论最小逻辑错误率。我们使用张量网络解码器实例化该框架，其能提供精确的最大似然解码及对所有噪声参数可解析求导的梯度。利用来自谷歌 Sycamore 处理器的电路级数据，且从无信息先验开始，优化过程恢复的噪声模型其逻辑错误率与谷歌独立表征的检测器错误模型的逻辑错误率相差在 2% 以内。学习到的噪声参数与参考噪声参数之间的均方误差在整个训练过程中呈现明显的整体下降，证实该方法恢复了具有物理意义的噪声结构，而非仅仅是恰好解码良好的参数。我们进一步证明该优化可通过热启动更新实时跟踪设备漂移，在无需重新表征的情况下，在合成演化噪声下保持近最优解码性能。该方法的公式与解码器无关，且自然可扩展至相关噪声模型。

英文摘要

Accurate noise models are essential for high-performance quantum error correction, yet characterizing the noise of a quantum device typically requires dedicated experiments. We present a variational framework that learns the noise model directly from quantum-error-correction syndrome and logical-observable data collected during error-corrected memory experiments. The fault-event probabilities are treated as variational parameters and optimized via gradient descent to minimize the binary cross-entropy between the decoder's predictions and experimental logical-observable outcomes. We prove that this objective is principled rather than ad hoc: a sufficiently expressive noise ansatz attains the information-theoretic minimum logical error rate. We instantiate this framework using a tensor-network decoder, which provides exact maximum-likelihood decoding and analytically differentiable gradients with respect to all noise parameters. Using circuit-level data from Google's Sycamore processor, and starting from an uninformed prior, the optimization recovers noise models whose logical error rates agree to within $2\%$ with those of Google's independently characterized detector error model. The mean squared error between the learned and reference noise parameters shows a clear overall decrease throughout training, confirming that the method recovers physically meaningful noise structure, not merely parameters that happen to decode well. We further demonstrate that the optimization can track device drifts in real time via warm-started updates, maintaining near-optimal decoding performance under synthetically evolving noise without the need for re-characterization. The approach is decoder-agnostic in its formulation and naturally extends to correlated noise models.

发表机构

• NVIDIA Corporation（英伟达公司）

机构由 AI 辅助整理，请以论文原文为准。