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arXiv 2302.01873quant-phcs.DS
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面向线性代数的量子比特高效型随机量子算法

Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra

Samson Wang, Sam McArdle, Mario Berta

AI总结:

该研究提出一类无需量子块编码与量子数据结构的随机量子算法,仅需log(N)+1量子比特即可实现矩阵函数采样,还可用于线性系统求解、量子态性质采样及多体系统Green函数计算。

摘要:

我们提出一类用于矩阵函数采样任务的随机量子算法,该算法无需使用量子块编码,也无需任何其他对矩阵元素的相干预言机访问。因此,我们对量子比特的使用纯粹是算法层面的,不需要额外的量子比特来构建量子数据结构。我们的算法基于经典数据结构启动,其中目标矩阵以Pauli基表示。对于N×N厄米矩阵,空间开销为log(N)+1个量子比特;在考虑等效的端到端问题时,根据矩阵结构的不同,其门复杂度可与采用规模最高达O(N²)的量子数据结构的前沿方法相当。在我们的框架内,我们提出了一种量子线性系统求解器,可对解向量的性质进行采样,同时还提出了用于采样哈密顿量基态和Gibbs态性质的算法。作为具体应用,我们结合这些子例程,提出了一种计算量子多体系统Green函数的方案。

英文摘要:

We propose a class of randomized quantum algorithms for the task of sampling from matrix functions, without the use of quantum block encodings or any other coherent oracle access to the matrix elements. As such, our use of qubits is purely algorithmic, and no additional qubits are required for quantum data structures. Our algorithms start from a classical data structure in which the matrix of interest is specified in the Pauli basis. For $N\times N$ Hermitian matrices, the space cost is $\log(N)+1$ qubits and depending on the structure of the matrices, the gate complexity can be comparable to state-of-the-art methods that use quantum data structures of up to size $O(N^2)$, when considering equivalent end-to-end problems. Within our framework, we present a quantum linear system solver that allows one to sample properties of the solution vector, as well as algorithms for sampling properties of ground states and Gibbs states of Hamiltonians. As a concrete application, we combine these sub-routines to present a scheme for calculating Green's functions of quantum many-body systems.

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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