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arXiv 1912.12366quant-phcs.DS
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基于变分量子本征求解器的近似图谱分解

Approximate Graph Spectral Decomposition with the Variational Quantum Eigensolver

Josh Payne, Mario Srouji

摘要:

谱图论是数学的一个分支,研究拉普拉斯矩阵和邻接矩阵的特征向量和特征值与其相关图之间的关系。变分量子本征求解器(VQE)算法是一种混合量子/经典算法,用于快速确定哈密顿量的基态,更一般地说,用于确定矩阵$M\in \mathbb{R}^{n\times n}$的最低本征值。伴随矩阵的谱分解有许多有趣的问题,如分块、嵌入和其他性质的确定。在本文中,我们将扩展的VQE算法来分析有向图和无向图的谱。我们评估的运行时间和准确性比较(经验和理论)之间的不同选择的参数,图形大小,图形密度和矩阵类型,并证明我们的方法的有效性Rigetti的QCS平台上的图形高达64顶点,找到特征值的邻接和拉普拉斯矩阵。最后,我们在附录中使用量子虚拟机(QVM)与经典性能进行直接比较,观察使用量子计算机运行时我们的算法的超多项式运行时改进。

英文摘要:

Spectral graph theory is a branch of mathematics that studies the relationships between the eigenvectors and eigenvalues of Laplacian and adjacency matrices and their associated graphs. The Variational Quantum Eigensolver (VQE) algorithm was proposed as a hybrid quantum/classical algorithm that is used to quickly determine the ground state of a Hamiltonian, and more generally, the lowest eigenvalue of a matrix $M\in \mathbb{R}^{n\times n}$. There are many interesting problems associated with the spectral decompositions of associated matrices, such as partitioning, embedding, and the determination of other properties. In this paper, we will expand upon the VQE algorithm to analyze the spectra of directed and undirected graphs. We evaluate runtime and accuracy comparisons (empirically and theoretically) between different choices of ansatz parameters, graph sizes, graph densities, and matrix types, and demonstrate the effectiveness of our approach on Rigetti's QCS platform on graphs of up to 64 vertices, finding eigenvalues of adjacency and Laplacian matrices. We finally make direct comparisons to classical performance with the Quantum Virtual Machine (QVM) in the appendix, observing a superpolynomial runtime improvement of our algorithm when run using a quantum computer.

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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