Quantum computation: Efficient network partitioning for large scale critical infrastructures

TL;DR

This paper proposes a quantum computing approach to efficiently solve network partitioning problems in critical infrastructures, leveraging quantum speedup in eigenvalue and eigenvector computations of graph Laplacians.

Contribution

It introduces a novel quantum method for network partitioning that improves computational efficiency over classical techniques for large-scale infrastructure analysis.

Findings

Quantum algorithms can speed up eigenvalue and eigenvector computations.

Potential for improved risk analysis in critical infrastructures.

Addresses classical computational constraints in large sparse graphs.

Abstract

Quantum computers are emerging as a viable alternative to tackle certain computational problems that are challenging for classical computers. With the rapid development of quantum hardware such as those based on trapped ions, there is practical motivation for identifying risk management problems that are efficiently solvable with these systems. Here we focus on network partitioning as a means for analyzing risk in critical infrastructures and present a quantum approach for its implementation. It is based on the potential speedup quantum computers can provide in the identification of eigenvalues and eigenvectors of sparse graph Laplacians, a procedure which is constrained by time and memory on classical computers.