泡利光锥：超越自相关的信息论误差 mitigation
The Pauli Lightcone: Information-Theoretic Error Mitigation Beyond the Autocorrelation
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中文总结 AI 辅助
该研究引入波映射作为无模型噪声诊断工具，结合多乘积公式（MPF）与利布-罗宾逊因果约束，在IBM重六边形晶格上恢复泡利权重场，可恢复高达55%的信息损失，指出需开展校准合成噪声实验。
中文摘要 AI 辅助
我们引入波映射（wavemap）：一种噪声效应的空间表征，为每个位点分配对应噪声水平的到达延迟l_γ(v)和交叉熵损失L_γ(v)。这些可观测量在光锥前沿是精确的，此时键维度χ较小，模拟最忠实。对由门和噪声构成的泡利转移矩阵的本征值分析证实，所研究的噪声是纯振幅阻尼：空间传播模式完全由门决定，这使波映射成为一种无模型的噪声诊断工具。我们应用多乘积公式（MPF），在满足利布-罗宾逊因果约束nMPF ≤ nnl的条件下，从含噪声样本中恢复无噪声泡利权重场。对前沿的时间自适应系数α(t)进行拟合，可恢复相对于最佳含噪声样本的高达55%的信息损失，利用了前沿处截断误差最小的特性。在具有异构硬件噪声的IBM重六边形晶格上，该方法识别出信息匮乏的区域，指出校准合成噪声是下一步所需的实验。
英文摘要
We introduce the wavemap: a spatial portrait of noise effects that assigns each site a per-noise-level arrival delay l_γ(v) and cross-entropy loss L_γ(v). These observables are exact at the lightcone frontier, where bond dimension χis small and the simulation is most faithful. Eigenvalue analysis of the composed gate-plus-noise Pauli transfer matrices confirms that the studied noise is pure amplitude damping: the spatial propagation pattern is entirely determined by the gate, making the wavemap a model-free noise diagnostic. We apply the multi-product formula (MPF) to recover the noiseless Pauli weight field from the noisy samples, subject to the Lieb-Robinson causal constraint nMPF <= nnl . Fitting time-adaptive coefficients α(t) over the frontier recovers up to 55% of the information loss relative to the best noisy sample, exploiting the fact that the frontier is where truncation error is smallest. On an IBM heavy-hex lattice with heterogeneous hardware noise the method identifies an information-starved regime, pointing to calibrated synthetic noise as the next required experiment.
发表机构
- Advanced Micro Devices, Inc.（超威半导体公司）
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