Addressing quantum's "fine print" with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Jessie M Henderson; John Kath; John K Golden; Allon G Percus; Daniel O'Malley

doi:10.1038/s41598-024-52759-0

Addressing quantum's "fine print" with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Sci Rep. 2024 Feb 13;14(1):3592. doi: 10.1038/s41598-024-52759-0.

Authors

Jessie M Henderson^#¹, John Kath^#², John K Golden³, Allon G Percus², Daniel O'Malley⁴

Affiliations

¹ Los Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA. jessieh@lanl.gov.
² Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA, 91711, USA.
³ Los Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA.
⁴ Los Alamos National Laboratory, EES-16, Los Alamos, NM, 87545, USA.

^# Contributed equally.

Abstract

Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.

Abstract

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