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

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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.

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Notes

  1. Of course, the values determined by FV did not necessarily agree with the actual values of \({\mathbf {k}}_{\mathrm{true}}\) due to the effects of noise.

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Authors and Affiliations

  1. Computational Earth Science, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    John K. Golden & Daniel O’Malley

  2. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD, 21250, USA

    Daniel O’Malley

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  1. John K. Golden
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  2. Daniel O’Malley
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Correspondence to John K. Golden.

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Golden, J.K., O’Malley, D. Pre- and post-processing in quantum-computational hydrologic inverse analysis. Quantum Inf Process 20, 176 (2021). https://doi.org/10.1007/s11128-021-03115-y

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