Efficient Quantum Algorithms for Analyzing Large Sparse Electrical Networks

Abstract

Analyzing large sparse electrical networks is a fundamental task in physics, electrical engineering and computer science. We propose two classes of quantum algorithms for this task. The first class is based on solving linear systems, and the second class is based on using quantum walks. These algorithms compute various electrical quantities, including voltages, currents, dissipated powers and effective resistances, in time $\mathrm{poly}(d,c,\log (N),1/\lambda ,1/\epsilon )$, where $N$ is the number of vertices in the network, $d$ is the maximum unweighted degree of the vertices, $c$ is the ratio of largest to smallest edge resistance, $\lambda$ is the spectral gap of the normalized Laplacian of the network, and $\epsilon$ is the accuracy. Furthermore, we show that the polynomial dependence on $1/\lambda$ is necessary. This implies that our algorithms are optimal up to polynomial factors and cannot be significantly improved.

40 pages, 2 figures. Final version

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Cited by in corpus (2)

• Quantum walk speedup of backtracking algorithms
• An HHL-Based Algorithm for Computing Hitting Probabilities of Quantum Random Walks