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arXiv 2402.09511quant-ph
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经典阴影的有偏估计量信道

Biased Estimator Channels for Classical Shadows

Zhenyu Cai, Adrian Chapman, Hamza Jnane, Bálint Koczor

AI总结:

本文提出一种通过重缩放传统经典阴影估计量来有意引入偏差的方法,以减少有限样本情况下的误差,且易于实现且无需量子资源,并在12量子位自旋环量子模拟任务中证明了其显著提高非局域扰动期望值估计效率的优势。

摘要:

从量子系统中提取经典信息至关重要,经典阴影使我们能用相对较少的测量提取大量信息。传统阴影估计量是无偏的,因此在无限样本极限下趋近真实均值。在本工作中,我们考虑一种有偏方案,通过重缩放传统经典阴影估计量来有意引入偏差,从而减少有限样本情况下的误差。该方法易于实现且不需要量子资源。我们分析证明了平均情况以及最坏和最好情况场景,并严格证明原则上对估计量进行偏置总是值得的。我们在12量子位自旋环问题的量子模拟任务中展示了该方法,并证明了使用我们的有偏方案估计非局域扰动的期望值可以显著提高效率。

英文摘要:

Extracting classical information from quantum systems is of fundamental importance, and classical shadows allow us to extract a large amount of information using relatively few measurements. Conventional shadow estimators are unbiased and thus approach the true mean in the infinite-sample limit. In this work, we consider a biased scheme, intentionally introducing a bias by rescaling the conventional classical shadows estimators can reduce the error in the finite-sample regime. The approach is straightforward to implement and requires no quantum resources. We analytically prove average case as well as worst- and best-case scenarios, and rigorously prove that it is, in principle, always worth biasing the estimators. We illustrate our approach in a quantum simulation task of a $12$-qubit spin-ring problem and demonstrate how estimating expected values of non-local perturbations can be significantly more efficient using our biased scheme.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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