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中国物理学会期刊

关于采样问题的量子优越性综述

CSTR: 32037.14.aps.70.20211428

Review on quantum advantages of sampling problems

CSTR: 32037.14.aps.70.20211428
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  • 利用量子态的叠加性和纠缠, 量子计算为显著地加速经典算法, 例如大数分解、求解线性方程组、量子多体系统模拟等问题, 提供了可能. 随着量子计算机硬件的快速发展, 探索量子计算超越经典计算极限方向的研究受到了越来越多的重视. 针对一类特定的问题, 现有的量子设备已经展现出超越经典计算机的能力. 但由于一些量子算法(诸如大数分解等问题)需要依赖于一个通用的大规模的容错的量子计算机, 考虑到现阶段的量子设备的量子比特数十分有限, 且容易与环境发生退相干, 近期的研究主要集中在探索基于含噪声的中等规模量子设备以及浅层量子线路的量子优越性. 一些采样问题被作为演示量子优越性的候选项提出. 本文介绍和总结了几个可以在现阶段的量子设备上实现的量子优越性问题, 并就其中两个备受关注的量子优越性问题—随机量子线路模拟和玻色采样及其衍生的采样问题的理论和实验进展、经典模拟算法等展开讨论. 随着上述两类量子优越性问题在超导和光学量子平台的实现, 我们预期当前和近期的量子设备将解决更多问题, 从而实现更一般的量子优势.

     

    Exploiting the coherence and entanglement of quantum many-qubit states, quantum computing can significantly surpass classical algorithms, making it possible to factor large numbers, solve linear equations, simulate many-body quantum systems, etc., in a reasonable time. With the rapid development of quantum computing hardware, many attention has been drawn to explore how quantum computers could go beyond the limit of classical computation. Owing to the need of a universal fault-tolerant quantum computer for many existing quantum algorithms, such as Shor’s factoring algorithm, and considering the limit of near-term quantum devices with small qubit numbers and short coherence times, many recent works focused on the exploration of demonstrating quantum advantages using noisy intermediate-scaled quantum devices and shallow circuits, and hence some sampling problems have been proposed as the candidates for quantum advantage demonstration. This review summarizes quantum advantage problems that are realizable on current quantum hardware. We focus on two notable problems—random circuit simulation and boson sampling—and consider recent theoretical and experimental progresses. After the respective demonstrations of these two types of quantum advantages on superconducting and optical quantum platforms, we expect current and near-term quantum devices could be employed for demonstrating quantum advantages in general problems.

     

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