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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 poly(d,c,log(N),1/λ,1/ε), 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, λ is the spectral gap of the normalized Laplacian of the network, and ε is the accuracy. Furthermore, we show that the polynomial dependence on 1/λ is necessary. This implies that our algorithms are optimal up to polynomial factors and cannot be significantly improved.

Publication Details

Authors
Publication Type
Journal Article
Year of Publication
2017
Journal
Quantum Info. Comput.
Volume
17
Date Published
09/2017
Pagination
987–1026
ISSN
1533-7146