Quantum Computing and Preconditioners for Hydrological Linear Systems

TL;DR

This paper explores quantum algorithms for solving large, ill-conditioned linear systems in hydrological modeling, introducing a new preconditioner that enhances quantum solver efficiency for fracture network simulations.

Contribution

The paper presents the inverse Laplacian preconditioner that improves the condition number scaling and enables quantum implementation for hydrological linear systems.

Findings

The inverse Laplacian preconditioner reduces the condition number scaling from O(N) to O(√N).

Existing quantum techniques are insufficient for ill-conditioned systems.

This work advances quantum linear system algorithms for real-world hydrological applications.

Abstract

Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from $O(N)$ to $O(\sqrt{N})$ and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.