论文标题
用于近期量子计算中经典提升的虚拟线性地图算法
Virtual linear map algorithm for classical boost in near-term quantum computing
论文作者
论文摘要
近年来见证的量子计算的快速进步引发了人们对开发可扩展的量子信息理论方法与大量子系统合作的广泛兴趣。例如，已经提出了几种方法来绕过断层扫描状态重建，但在一定程度上保留了估计先前测量的给定状态的多个物理特性的能力。在本文中，我们介绍了虚拟线性地图算法（VILMA），一种新方法，一种新方法，不仅可以使用经典的信息完整测量结果进行经典后处理来估算多个操作员的平均水平，还可以为此做到这一点，以便为了测量的参考状态下的图像，在不一定是Physemary byspoly byshrophys byshrophys byshrophys，$ k $ k $ - $ $ -Local maps下的参考状态下。我们还表明，Vilma允许通过有效的线性程序序列对虚拟电路进行各种优化。最后，我们探讨了算法的纯粹经典版本，其中输入状态是具有经典表示的状态，并表明该方法可以准备多体汉密尔顿人的基态。
The rapid progress in quantum computing witnessed in recent years has sparked widespread interest in developing scalable quantum information theoretic methods to work with large quantum systems. For instance, several approaches have been proposed to bypass tomographic state reconstruction, and yet retain to a certain extent the capability to estimate multiple physical properties of a given state previously measured. In this paper, we introduce the Virtual Linear Map Algorithm (VILMA), a new method that enables not only to estimate multiple operator averages using classical post-processing of informationally complete measurement outcomes, but also to do so for the image of the measured reference state under low-depth circuits of arbitrary, not necessarily physical, $k$-local maps. We also show that VILMA allows for the variational optimisation of the virtual circuit through sequences of efficient linear programs. Finally, we explore the purely classical version of the algorithm, in which the input state is a state with a classically efficient representation, and show that the method can prepare ground states of many-body Hamiltonians.
