Pre- and post-processing in quantum-computational hydrologic inverse analysis

TL;DR

This paper investigates the challenges of using quantum annealers for hydrological inverse problems involving heterogeneous aquifers and introduces pre- and post-processing techniques to enhance performance and understanding of problem scaling.

Contribution

It introduces novel pre- and post-processing methods to improve quantum annealing for complex hydrological inverse problems with heterogeneous materials.

Findings

Enhanced quantum annealing performance on real-world hydrological problems.

Analyzed the scaling behavior of inverse problems in hydrology.

Identified a class of challenging problems suitable for quantum annealers.

Abstract

It was recently shown that certain subsurface hydrological inverse problems -- here framed as determining the composition of an aquifer from pressure readings -- can be solved on a quantum annealer. However, the quantum annealer performance suffered when solving problems where the aquifer was composed of materials with vastly different permeability, which is often encountered in practice. In this paper, we study why this regime is difficult and use several pre- and post-processing tools to address these issues. This study has three benefits: it improves quantum annealing performance for real-world problems in hydrology, it studies the scaling behavior for these problems (which were previously studied at a fixed size), and it elucidates a challenging class of problems that are amenable to quantum annealers.