经典增强零噪声外推法
Classically Augmented Zero-Noise Extrapolation
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中文总结 AI 辅助
研究量子误差缓解的量子-经典混合方法，提出经典增强零噪声外推法，用经典模拟估计取代高噪声理查森外推节点，推导最优采样下的方差 reduction，经数值验证，截断偏差小时均方误差降低。
中文摘要 AI 辅助
我们研究了一种用于量子误差缓解的量子-经典混合方法。我们提出了经典增强零噪声外推法，这是一种混合误差缓解方法，其中高噪声理查森外推节点被经典模拟估计所取代。这些经典节点具有可忽略的采样方差，但会引入确定性模拟偏差。我们推导了在最优采样分配下的方差 reduction，并表明，对于线性节点间距和固定索引截断，系数级 reduction 可以是指数级的。我们使用泡利传播模拟对预测进行了数值验证，并证明了当截断偏差足够小时，均方误差会降低。
英文摘要
We investigate a hybrid quantum-classical approach to quantum error mitigation. We propose Classically Augmented Zero-Noise Extrapolation, a hybrid error-mitigation method in which high-noise Richardson extrapolation nodes are replaced by classically simulated estimates. These classical nodes have negligible sampling variance but introduce deterministic simulation bias. We derive the resulting variance reduction under optimal shot allocation and show that, for linear node spacings and fixed index cutoff, the coefficient-level reduction can be exponential. We validate the prediction numerically using Pauli-propagation simulations and demonstrate a reduction in mean-squared error when the truncation bias is sufficiently small.
