Quantum algorithms for geologic fracture networks
Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many prac...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29643-4 |
| _version_ | 1797864701161373696 |
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| author | Jessie M. Henderson Marianna Podzorova M. Cerezo John K. Golden Leonard Gleyzer Hari S. Viswanathan Daniel O’Malley |
| author_facet | Jessie M. Henderson Marianna Podzorova M. Cerezo John K. Golden Leonard Gleyzer Hari S. Viswanathan Daniel O’Malley |
| author_sort | Jessie M. Henderson |
| collection | DOAJ |
| description | Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning. |
| first_indexed | 2024-04-09T22:56:15Z |
| format | Article |
| id | doaj.art-857c507a01ee43fd843aeecca6e15796 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:56:15Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-857c507a01ee43fd843aeecca6e157962023-03-22T11:13:53ZengNature PortfolioScientific Reports2045-23222023-02-0113111310.1038/s41598-023-29643-4Quantum algorithms for geologic fracture networksJessie M. Henderson0Marianna Podzorova1M. Cerezo2John K. Golden3Leonard Gleyzer4Hari S. Viswanathan5Daniel O’Malley6Los Alamos National LaboratoryLos Alamos National LaboratoryLos Alamos National LaboratoryLos Alamos National LaboratoryLos Alamos National LaboratoryLos Alamos National LaboratoryLos Alamos National LaboratoryAbstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.https://doi.org/10.1038/s41598-023-29643-4 |
| spellingShingle | Jessie M. Henderson Marianna Podzorova M. Cerezo John K. Golden Leonard Gleyzer Hari S. Viswanathan Daniel O’Malley Quantum algorithms for geologic fracture networks Scientific Reports |
| title | Quantum algorithms for geologic fracture networks |
| title_full | Quantum algorithms for geologic fracture networks |
| title_fullStr | Quantum algorithms for geologic fracture networks |
| title_full_unstemmed | Quantum algorithms for geologic fracture networks |
| title_short | Quantum algorithms for geologic fracture networks |
| title_sort | quantum algorithms for geologic fracture networks |
| url | https://doi.org/10.1038/s41598-023-29643-4 |
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