论文标题
强大的影子估计
Robust shadow estimation
论文作者
论文摘要
有效估计大型且强耦合量子系统的性能是多体物理学和量子信息理论的核心重点。虽然量子计算机向许多此类任务承诺加速，但近期设备容易降低噪声，这通常会降低此类估计的准确性。在这里，我们展示了如何减轻Huang，Kueng和Preskill最近提出的影子估计协议中的错误。通过将实验友好的校准阶段添加到标准的阴影估计方案中，我们的稳健阴影估计算法可以获得对量子系统的经典阴影的无偏估计，因此在实验条件下只有最小的假设，以样品效率和噪声方式提取许多有用的特性。我们对协议的样本复杂性给出了严格的界限，并通过几个数值实验证明了其性能。
Efficiently estimating properties of large and strongly coupled quantum systems is a central focus in many-body physics and quantum information theory. While quantum computers promise speedups for many such tasks, near-term devices are prone to noise that will generally reduce the accuracy of such estimates. Here we show how to mitigate errors in the shadow estimation protocol recently proposed by Huang, Kueng, and Preskill. By adding an experimentally friendly calibration stage to the standard shadow estimation scheme, our robust shadow estimation algorithm can obtain an unbiased estimate of the classical shadow of a quantum system and hence extract many useful properties in a sample-efficient and noise-resilient manner given only minimal assumptions on the experimental conditions. We give rigorous bounds on the sample complexity of our protocol and demonstrate its performance with several numerical experiments.
