标题： 去随机化浅层阴影：使用有限深度电路的高效泡利学习 显示英文标题

标题： Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits

摘要： 高效估计大量非对易可观测量是许多量子科学任务的重要子程序。 我们提出了去随机化浅层阴影（DSS）算法，用于使用浅层电路旋转到测量基中，以高效学习大量非对易可观测量。 利用张量网络技术确保经典资源的多项式扩展，我们的算法输出一组浅层测量电路，该电路近似最小化给定一组泡利字符串的样本复杂度。 我们在数值上展示了与最先进的技术相比，在量子化学基准的能量估计和量子多体系统的验证中的系统性改进，并且我们观察到随着允许更深层的测量电路，DSS的性能持续提高。 这些结果表明，除了作为高效、低深度的独立算法外，DSS还可以使需要估计多个非对易可观测量的更大量子算法受益。 显示英文摘要

摘要： Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polynomial scaling of classical resources, our algorithm outputs a set of shallow measurement circuits that approximately minimizes the sample complexity of estimating a given set of Pauli strings. We numerically demonstrate systematic improvement, in comparison with state-of-the-art techniques, for energy estimation of quantum chemistry benchmarks and verification of quantum many-body systems, and we observe DSS's performance consistently improves as one allows deeper measurement circuits. These results indicate that in addition to being an efficient, low-depth, stand-alone algorithm, DSS can also benefit many larger quantum algorithms requiring estimation of multiple non-commuting observables.