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

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Jessie M Henderson *Los Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA. jessieh@lanl.gov.

John Kath *Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA, 91711, USA.

John K GoldenLos Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA.

Allon G PercusInstitute of Mathematical Sciences, Claremont Graduate University, Claremont, CA, 91711, USA.

Daniel O'MalleyLos Alamos National Laboratory, EES-16, Los Alamos, NM, 87545, USA.

Funding

Basic Energy Sciences LANLE3W1Los Alamos National Laboratory LDRD 20220077ER

6 · The paper itself

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.

Identifiers

PMID38351145

DOI10.1038/s41598-024-52759-0

PMCPMC10864371

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