Quantum annealing algorithms for Boolean tensor networks
Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a...
Saved in:
| Published in: | Scientific reports Vol. 12; no. 1; pp. 8539 - 19 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
20.05.2022
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2045-2322, 2045-2322 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e.,
{
0
,
1
}
) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. |
|---|---|
| AbstractList | Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., [Formula: see text]) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor's with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., [Formula: see text]) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor's with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer.Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., [Formula: see text]) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor's with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., $$\{0, 1\}$$ 0,1) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., {0,1}) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., $$\{0, 1\}$$ { 0 , 1 } ) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Abstract Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., $$\{0, 1\}$$ { 0 , 1 } ) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., { 0 , 1 } ) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer. |
| ArticleNumber | 8539 |
| Author | Pelofske, Elijah Hahn, Georg Alexandrov, Boian S. O’Malley, Daniel Djidjev, Hristo N. |
| Author_xml | – sequence: 1 givenname: Elijah surname: Pelofske fullname: Pelofske, Elijah email: epelofske@lanl.gov organization: Los Alamos National Laboratory – sequence: 2 givenname: Georg surname: Hahn fullname: Hahn, Georg organization: Harvard T.H. Chan School of Public Health – sequence: 3 givenname: Daniel surname: O’Malley fullname: O’Malley, Daniel organization: Los Alamos National Laboratory – sequence: 4 givenname: Hristo N. surname: Djidjev fullname: Djidjev, Hristo N. organization: Los Alamos National Laboratory, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences – sequence: 5 givenname: Boian S. surname: Alexandrov fullname: Alexandrov, Boian S. organization: Los Alamos National Laboratory |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35595786$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1869036$$D View this record in Osti.gov |
| BookMark | eNp9Ustu1TAUjFARLaU_wAJFsGET8CuOvUEqFY9KlRASrK0Tx871JbGL7YD69_W9aaHtot74NTOeczzPqwMfvKmqlxi9w4iK94nhVooGEdJgwjFu5JPqiCDWNoQScnBnfVidpLRFZbREMiyfVYe0bWXbCX5Uie8L-LzMNXhvYHJ-rGEaQ3R5M6fahlh_DGEy4OtsfCpbb_LfEH-lF9VTC1MyJzfzcfXz86cfZ1-bi29fzs9OLxrd8i43vQBCNTFCg6RaMMsM48All7bvBeksFdhaxnjXDQL4gAaKMWW0lAgD7Qd6XJ2vukOArbqMboZ4pQI4tT8IcVQQs9OTUR01WlNqCR6AWUBykEhb3hlJoBUdLVofVq3LpZ_NoI3PEaZ7ovdvvNuoMfxREpPiaCfwehUIKTuVtMtGb3QordNZYcEloryA3t68EsPvxaSsZpe0mSbwJixJEV6KFaIjqEDfPIBuwxJ96ecehSQSYif46q7tf35vf7EAxArQMaQUjVXFGWQXdlW4SWGkdplRa2ZUyYzaZ0bJQiUPqLfqj5LoSkoF7EcT_9t-hHUNfXTSUg |
| CitedBy_id | crossref_primary_10_1007_s11128_024_04512_9 crossref_primary_10_1088_2058_9565_ad6eb3 crossref_primary_10_1002_qute_202300104 crossref_primary_10_1038_s41534_024_00825_w crossref_primary_10_1103_PhysRevResearch_5_023151 crossref_primary_10_1038_s41598_022_08394_8 crossref_primary_10_1088_2058_9565_accbe6 crossref_primary_10_1038_s44335_025_00032_6 crossref_primary_10_1103_PhysRevResearch_5_013224 crossref_primary_10_1371_journal_pone_0304594 crossref_primary_10_1007_s10732_025_09556_3 crossref_primary_10_1016_j_jii_2024_100663 |
| Cites_doi | 10.1137/06066518X 10.1109/ICRC2020.2020.00002 10.1038/s41598-022-08394-8 10.5281/zenodo.5773480 10.1038/44565 10.1109/TKDE.2008.53 10.1109/ICDM.2011.28 10.1016/S0166-218X(01)00341-9 10.1103/RevModPhys.80.1061 10.1016/0196-6774(90)90014-6 10.1007/s11265-018-1357-8 10.1007/BF01886518 10.1007/978-3-030-97549-4_40 10.1145/3310273.3321562 10.1109/MC.2019.2908836 10.1371/journal.pone.0244026 10.25080/TCWV9851 10.1063/1.3672009 10.1145/3459606 10.1137/090748330 10.1016/0009-2614(94)00117-0 10.1088/1361-6633/ab85b8 10.1002/sapm192761164 10.1137/07070111X 10.1145/3149526.3149531 10.1137/1035134 10.1007/BF02289464 10.1371/journal.pone.0206653 10.3389/fphy.2014.00005 10.1109/MCSE.2007.55 10.1137/090752286 10.5281/zenodo.4876527 10.1103/RevModPhys.90.015002 |
| ContentType | Journal Article |
| Copyright | This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022 2022. The Author(s). This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022 – notice: 2022. The Author(s). – notice: This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| CorporateAuthor | Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) |
| CorporateAuthor_xml | – sequence: 0 name: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 OTOTI 5PM DOA |
| DOI | 10.1038/s41598-022-12611-9 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic OSTI.GOV PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology Computer Science |
| EISSN | 2045-2322 |
| EndPage | 19 |
| ExternalDocumentID | oai_doaj_org_article_73ecc33f21da4fa09d90cf67e92a5873 PMC9123033 1869036 35595786 10_1038_s41598_022_12611_9 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Science and Education for Smart Growth Operational Program (2014-2020) grantid: BG05M2OP001-1.001-0003 – fundername: Laboratory Directed Research and Development grantid: 20190020DR funderid: http://dx.doi.org/10.13039/100007000 – fundername: Laboratory Directed Research and Development grantid: 20190020DR – fundername: ; grantid: BG05M2OP001-1.001-0003 – fundername: ; grantid: 20190020DR |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB NPM 7XB 8FK K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO OTOTI 5PM |
| ID | FETCH-LOGICAL-c567t-b8a23c2e8ca93c84f4e46a6969fbb827f381ff44677d8a6d0d311343038ad3bd3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 13 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000798349300029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Fri Oct 03 12:43:35 EDT 2025 Tue Nov 04 02:01:47 EST 2025 Thu Dec 05 06:30:47 EST 2024 Fri Sep 05 11:45:35 EDT 2025 Tue Oct 07 07:38:35 EDT 2025 Thu Apr 03 07:03:27 EDT 2025 Tue Nov 18 22:11:22 EST 2025 Sat Nov 29 06:26:05 EST 2025 Fri Feb 21 02:39:34 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2022. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c567t-b8a23c2e8ca93c84f4e46a6969fbb827f381ff44677d8a6d0d311343038ad3bd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 LA-UR-21-27414 USDOE Laboratory Directed Research and Development (LDRD) Program 89233218CNA000001; 20190020DR; BG05M2OP001-1.001-0003 |
| ORCID | 000000032673796X 0000000304323088 0000000192868824 0000000186364603 |
| OpenAccessLink | https://doaj.org/article/73ecc33f21da4fa09d90cf67e92a5873 |
| PMID | 35595786 |
| PQID | 2667090886 |
| PQPubID | 2041939 |
| PageCount | 19 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_73ecc33f21da4fa09d90cf67e92a5873 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9123033 osti_scitechconnect_1869036 proquest_miscellaneous_2667788720 proquest_journals_2667090886 pubmed_primary_35595786 crossref_citationtrail_10_1038_s41598_022_12611_9 crossref_primary_10_1038_s41598_022_12611_9 springer_journals_10_1038_s41598_022_12611_9 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-20 |
| PublicationDateYYYYMMDD | 2022-05-20 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-20 day: 20 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England – name: United States |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2022 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – sequence: 0 name: Nature Publishing Group – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Biamonte, J. Lectures on Quantum Tensor Networks. arXiv:1912.10049 (2019). LucasAIsing formulations of many NP problemsFront. Phys.2014212710.3389/fphy.2014.00005 BorosEHammerPPseudo-Boolean optimizationDiscret. Appl. Math.2002123155225192233410.1016/S0166-218X(01)00341-9 De SilvaVLimL-HTensor rank and the ill-posedness of the best low-rank approximation problemSIAM J. Matrix Anal. Appl.20083010841127244744410.1137/06066518X PenroseRApplications of negative dimensional tensorsCombin. Math. Appl.197112212442816570216.43502 LeeDDSeungHSLearning the parts of objects by non-negative matrix factorizationNature19994017887911999Natur.401..788L1:CAS:528:DyaK1MXntFartrg%3D10.1038/44565 D-Wave Systems. Dimod Github and Uniform Torque Compensation and Create a binary quadratic model from a higher order polynomial Github and Minorminer Github (2020). Syed, A. M., Qazi, S. & Gillis, N. Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction. http://arxiv.org/abs/1807.04020 (2018). PelofskeEHahnGO’MalleyDDjidjevHNAlexandrovBSLirkovIMargenovSBoolean hierarchical tucker networks on quantum annealersLarge-Scale Scientific Computing2022Springer35135810.1007/978-3-030-97549-4_40 The Nimfa developers. Nonnegative Double Singular Value Decomposition (2016). JüngerMQuantum annealing versus digital computing: An experimental comparisonJ. Exp. Algorithmics (JEA)202126130428615310.1145/3459606 DasAChakrabartiBKColloquium: Quantum annealing and analog quantum computationRev. Mod. Phys.200880106110812008RvMP...80.1061D244372110.1103/RevModPhys.80.10611205.81058 Caswell, T. A. et al. matplotlib/matplotlib: Rel: v3.5.1. https://doi.org/10.5281/zenodo.5773480 (2021). AlbashTLidarDAAdiabatic quantum computationRev. Mod. Phys.2018902018RvMP...90a5002A378842410.1103/RevModPhys.90.015002 Pelofske, E., Hahn, G. & Djidjev, H. Solving large Minimum Vertex Cover problems on a quantum annealer. In Proceedings of the Computing Frontiers Conference CF’19. arXiv:1904.00051, 1–16 (2019). MiettinenPMielikäinenTGionisADasGMannilaHThe discrete basis problemIEEE Trans. Knowl. Data Eng.2008201348136210.1109/TKDE.2008.53 HitchcockFLThe expression of a tensor or a polyadic as a sum of productsJ. Math. Phys.1927616418910.1002/sapm192761164 GoldenJO’MalleyDReverse annealing for nonnegative/binary matrix factorizationPLoS ONE2021161:CAS:528:DC%2BB3MXhtlSltL4%3D10.1371/journal.pone.0244026 HåstadJTensor rank is np-completeJ. Algorithms199011644654107945510.1016/0196-6774(90)90014-6 McGeochCCHarrisRReinhardtSPBunykPIPractical annealing-based quantum computingComputer201952384610.1109/MC.2019.2908836 EverettBAn Introduction to Latent Variable Models2013Springer KoldaTGBaderBWTensor decompositions and applicationsSIAM Rev.2009514555002009SIAMR..51..455K253505610.1137/07070111X Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkx. In Varoquaux, G., Vaught, T. & Millman, J. (eds.) Proceedings of the 7th Python in Science Confeence, pp. 11 – 15 (Pasadena, 2008). GrantEKHumbleTSBenchmarking embedded chain breaking in quantum annealingQuant. Sci. Technol.20211112 ChapuisGDjidjevHHahnGRizkGFinding maximum cliques on the D-wave quantum annealerJ. Signal Process. Syst.20199136337710.1007/s11265-018-1357-8 StewartGWOn the early history of the singular value decompositionSIAM Rev.199335551566124791610.1137/1035134 OseledetsIA New Tensor Decomposition. Doklady Mathematics2009Pleiades Publishing Ltd4954961183.15023 OseledetsIVTyrtyshnikovEEBreaking the curse of dimensionality, or how to use svd in many dimensionsSIAM J. Sci. Comput.20093137443759255656010.1137/090748330 Ushijima-Mwesigwa, H., Negre, C. F. & Mniszewski, S. M. Graph partitioning using quantum annealing on the d-wave system. In Proceedings of the Second International Workshop on Post Moores Era Supercomputing, 22–29 (2017). Pelofske, E. lanl/pyqbtns: Release v1.0.0. https://doi.org/10.5281/zenodo.4876527 (2021). HaukePKatzgraberHGLechnerWNishimoriHOliverWDPerspectives of quantum annealing: Methods and implementationsRep. Prog. Phys.2020832020RPPh...83e4401H1:CAS:528:DC%2BB3cXitFOltLjN10.1088/1361-6633/ab85b8 TuckerLRSome mathematical notes on three-mode factor analysisPsychometrika1966312793112053951:STN:280:DyaF287mtFSjtw%3D%3D10.1007/BF02289464 FeynmanRPQuantum mechanical computersFound. Phys.1986165075311986FoPh...16..507F89503510.1007/BF01886518 OseledetsIVTensor-train decompositionSIAM J. Sci. Comput.20113322952317283753310.1137/090752286 Miettinen, P. Boolean tensor factorizations. In IEEE 11th Intl Conference on Data Mining, 447–456 (IEEE, 2011). O’MalleyDVesselinovVAlexandrovBAlexandrovLNonnegative/binary matrix factorization with a d-wave quantum annealerPLoS ONE20181310.1371/journal.pone.0206653 HunterJDMatplotlib: A 2d graphics environmentComput. Sci. Eng.20079909510.1109/MCSE.2007.55 O’Malley, D., Djidjev, H. N. & Alexandrov, B. S. Tucker-1 Boolean Tensor Factorization with Quantum Annealers. In Proceedings of the 2020 International Conference on Rebooting Computing (ICRC), 58–65 (2020). FinnilaAGomezMSebenikCStensonCDollJQuantum annealing: A new method for minimizing multidimensional functionsChem. Phys. Lett.19942193433481994CPL...219..343F1:CAS:528:DyaK2cXisFelsbg%3D10.1016/0009-2614(94)00117-0 Cai, J., Macready, W. G. & Roy, A. A practical heuristic for finding graph minors (2014). BiamonteJDClarkSRJakschDCategorical tensor network statesAIP Adv.201112011AIPA....1d2172B10.1063/1.3672009 SpearmanCGeneral intelligence, objectively determined and measuredAm. J. Psychol.196115110 PelofskeEHahnGDjidjevHNParallel quantum annealingSci. Rep.20221244992022NatSR..12.4499P1:CAS:528:DC%2BB38Xnt1ymsLY%3D10.1038/s41598-022-08394-8352967218927114 IV Oseledets (12611_CR14) 2009; 31 RP Feynman (12611_CR13) 1986; 16 E Pelofske (12611_CR32) 2022 JD Hunter (12611_CR37) 2007; 9 A Lucas (12611_CR28) 2014; 2 12611_CR36 12611_CR17 12611_CR39 12611_CR16 E Pelofske (12611_CR31) 2022; 12 12611_CR38 12611_CR33 P Hauke (12611_CR35) 2020; 83 I Oseledets (12611_CR11) 2009 D O’Malley (12611_CR29) 2018; 13 IV Oseledets (12611_CR15) 2011; 33 M Jünger (12611_CR27) 2021; 26 J Golden (12611_CR30) 2021; 16 J Håstad (12611_CR9) 1990; 11 12611_CR40 E Boros (12611_CR20) 2002; 123 T Albash (12611_CR34) 2018; 90 A Finnila (12611_CR23) 1994; 219 12611_CR41 CC McGeoch (12611_CR21) 2019; 52 EK Grant (12611_CR42) 2021; 1 12611_CR26 FL Hitchcock (12611_CR8) 1927; 6 12611_CR43 LR Tucker (12611_CR7) 1966; 31 12611_CR24 JD Biamonte (12611_CR18) 2011; 1 B Everett (12611_CR1) 2013 P Miettinen (12611_CR5) 2008; 20 A Das (12611_CR22) 2008; 80 GW Stewart (12611_CR3) 1993; 35 DD Lee (12611_CR4) 1999; 401 R Penrose (12611_CR12) 1971; 1 12611_CR19 G Chapuis (12611_CR25) 2019; 91 C Spearman (12611_CR2) 1961; 15 TG Kolda (12611_CR6) 2009; 51 V De Silva (12611_CR10) 2008; 30 |
| References_xml | – reference: HitchcockFLThe expression of a tensor or a polyadic as a sum of productsJ. Math. Phys.1927616418910.1002/sapm192761164 – reference: HunterJDMatplotlib: A 2d graphics environmentComput. Sci. Eng.20079909510.1109/MCSE.2007.55 – reference: Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkx. In Varoquaux, G., Vaught, T. & Millman, J. (eds.) Proceedings of the 7th Python in Science Confeence, pp. 11 – 15 (Pasadena, 2008). – reference: OseledetsIVTensor-train decompositionSIAM J. Sci. Comput.20113322952317283753310.1137/090752286 – reference: ChapuisGDjidjevHHahnGRizkGFinding maximum cliques on the D-wave quantum annealerJ. Signal Process. Syst.20199136337710.1007/s11265-018-1357-8 – reference: GoldenJO’MalleyDReverse annealing for nonnegative/binary matrix factorizationPLoS ONE2021161:CAS:528:DC%2BB3MXhtlSltL4%3D10.1371/journal.pone.0244026 – reference: KoldaTGBaderBWTensor decompositions and applicationsSIAM Rev.2009514555002009SIAMR..51..455K253505610.1137/07070111X – reference: Cai, J., Macready, W. G. & Roy, A. A practical heuristic for finding graph minors (2014). – reference: PelofskeEHahnGDjidjevHNParallel quantum annealingSci. Rep.20221244992022NatSR..12.4499P1:CAS:528:DC%2BB38Xnt1ymsLY%3D10.1038/s41598-022-08394-8352967218927114 – reference: GrantEKHumbleTSBenchmarking embedded chain breaking in quantum annealingQuant. Sci. Technol.20211112 – reference: Syed, A. M., Qazi, S. & Gillis, N. Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction. http://arxiv.org/abs/1807.04020 (2018). – reference: StewartGWOn the early history of the singular value decompositionSIAM Rev.199335551566124791610.1137/1035134 – reference: OseledetsIA New Tensor Decomposition. Doklady Mathematics2009Pleiades Publishing Ltd4954961183.15023 – reference: AlbashTLidarDAAdiabatic quantum computationRev. Mod. Phys.2018902018RvMP...90a5002A378842410.1103/RevModPhys.90.015002 – reference: O’MalleyDVesselinovVAlexandrovBAlexandrovLNonnegative/binary matrix factorization with a d-wave quantum annealerPLoS ONE20181310.1371/journal.pone.0206653 – reference: SpearmanCGeneral intelligence, objectively determined and measuredAm. J. Psychol.196115110 – reference: Caswell, T. A. et al. matplotlib/matplotlib: Rel: v3.5.1. https://doi.org/10.5281/zenodo.5773480 (2021). – reference: FeynmanRPQuantum mechanical computersFound. Phys.1986165075311986FoPh...16..507F89503510.1007/BF01886518 – reference: DasAChakrabartiBKColloquium: Quantum annealing and analog quantum computationRev. Mod. Phys.200880106110812008RvMP...80.1061D244372110.1103/RevModPhys.80.10611205.81058 – reference: JüngerMQuantum annealing versus digital computing: An experimental comparisonJ. Exp. Algorithmics (JEA)202126130428615310.1145/3459606 – reference: De SilvaVLimL-HTensor rank and the ill-posedness of the best low-rank approximation problemSIAM J. Matrix Anal. Appl.20083010841127244744410.1137/06066518X – reference: D-Wave Systems. Dimod Github and Uniform Torque Compensation and Create a binary quadratic model from a higher order polynomial Github and Minorminer Github (2020). – reference: HåstadJTensor rank is np-completeJ. Algorithms199011644654107945510.1016/0196-6774(90)90014-6 – reference: Biamonte, J. Lectures on Quantum Tensor Networks. arXiv:1912.10049 (2019). – reference: FinnilaAGomezMSebenikCStensonCDollJQuantum annealing: A new method for minimizing multidimensional functionsChem. Phys. Lett.19942193433481994CPL...219..343F1:CAS:528:DyaK2cXisFelsbg%3D10.1016/0009-2614(94)00117-0 – reference: BiamonteJDClarkSRJakschDCategorical tensor network statesAIP Adv.201112011AIPA....1d2172B10.1063/1.3672009 – reference: PenroseRApplications of negative dimensional tensorsCombin. Math. Appl.197112212442816570216.43502 – reference: LeeDDSeungHSLearning the parts of objects by non-negative matrix factorizationNature19994017887911999Natur.401..788L1:CAS:528:DyaK1MXntFartrg%3D10.1038/44565 – reference: EverettBAn Introduction to Latent Variable Models2013Springer – reference: TuckerLRSome mathematical notes on three-mode factor analysisPsychometrika1966312793112053951:STN:280:DyaF287mtFSjtw%3D%3D10.1007/BF02289464 – reference: BorosEHammerPPseudo-Boolean optimizationDiscret. Appl. Math.2002123155225192233410.1016/S0166-218X(01)00341-9 – reference: The Nimfa developers. Nonnegative Double Singular Value Decomposition (2016). – reference: O’Malley, D., Djidjev, H. N. & Alexandrov, B. S. Tucker-1 Boolean Tensor Factorization with Quantum Annealers. In Proceedings of the 2020 International Conference on Rebooting Computing (ICRC), 58–65 (2020). – reference: Ushijima-Mwesigwa, H., Negre, C. F. & Mniszewski, S. M. Graph partitioning using quantum annealing on the d-wave system. In Proceedings of the Second International Workshop on Post Moores Era Supercomputing, 22–29 (2017). – reference: OseledetsIVTyrtyshnikovEEBreaking the curse of dimensionality, or how to use svd in many dimensionsSIAM J. Sci. Comput.20093137443759255656010.1137/090748330 – reference: McGeochCCHarrisRReinhardtSPBunykPIPractical annealing-based quantum computingComputer201952384610.1109/MC.2019.2908836 – reference: MiettinenPMielikäinenTGionisADasGMannilaHThe discrete basis problemIEEE Trans. Knowl. Data Eng.2008201348136210.1109/TKDE.2008.53 – reference: LucasAIsing formulations of many NP problemsFront. Phys.2014212710.3389/fphy.2014.00005 – reference: Miettinen, P. Boolean tensor factorizations. In IEEE 11th Intl Conference on Data Mining, 447–456 (IEEE, 2011). – reference: HaukePKatzgraberHGLechnerWNishimoriHOliverWDPerspectives of quantum annealing: Methods and implementationsRep. Prog. Phys.2020832020RPPh...83e4401H1:CAS:528:DC%2BB3cXitFOltLjN10.1088/1361-6633/ab85b8 – reference: Pelofske, E., Hahn, G. & Djidjev, H. Solving large Minimum Vertex Cover problems on a quantum annealer. In Proceedings of the Computing Frontiers Conference CF’19. arXiv:1904.00051, 1–16 (2019). – reference: Pelofske, E. lanl/pyqbtns: Release v1.0.0. https://doi.org/10.5281/zenodo.4876527 (2021). – reference: PelofskeEHahnGO’MalleyDDjidjevHNAlexandrovBSLirkovIMargenovSBoolean hierarchical tucker networks on quantum annealersLarge-Scale Scientific Computing2022Springer35135810.1007/978-3-030-97549-4_40 – volume: 30 start-page: 1084 year: 2008 ident: 12611_CR10 publication-title: SIAM J. Matrix Anal. Appl. doi: 10.1137/06066518X – ident: 12611_CR36 – ident: 12611_CR17 doi: 10.1109/ICRC2020.2020.00002 – ident: 12611_CR40 – volume: 12 start-page: 4499 year: 2022 ident: 12611_CR31 publication-title: Sci. Rep. doi: 10.1038/s41598-022-08394-8 – ident: 12611_CR38 doi: 10.5281/zenodo.5773480 – volume-title: An Introduction to Latent Variable Models year: 2013 ident: 12611_CR1 – volume: 401 start-page: 788 year: 1999 ident: 12611_CR4 publication-title: Nature doi: 10.1038/44565 – volume: 20 start-page: 1348 year: 2008 ident: 12611_CR5 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2008.53 – ident: 12611_CR16 doi: 10.1109/ICDM.2011.28 – volume: 123 start-page: 155 year: 2002 ident: 12611_CR20 publication-title: Discret. Appl. Math. doi: 10.1016/S0166-218X(01)00341-9 – start-page: 495 volume-title: A New Tensor Decomposition. Doklady Mathematics year: 2009 ident: 12611_CR11 – volume: 80 start-page: 1061 year: 2008 ident: 12611_CR22 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.80.1061 – volume: 11 start-page: 644 year: 1990 ident: 12611_CR9 publication-title: J. Algorithms doi: 10.1016/0196-6774(90)90014-6 – volume: 91 start-page: 363 year: 2019 ident: 12611_CR25 publication-title: J. Signal Process. Syst. doi: 10.1007/s11265-018-1357-8 – volume: 16 start-page: 507 year: 1986 ident: 12611_CR13 publication-title: Found. Phys. doi: 10.1007/BF01886518 – ident: 12611_CR19 – start-page: 351 volume-title: Large-Scale Scientific Computing year: 2022 ident: 12611_CR32 doi: 10.1007/978-3-030-97549-4_40 – ident: 12611_CR26 doi: 10.1145/3310273.3321562 – volume: 52 start-page: 38 year: 2019 ident: 12611_CR21 publication-title: Computer doi: 10.1109/MC.2019.2908836 – volume: 16 year: 2021 ident: 12611_CR30 publication-title: PLoS ONE doi: 10.1371/journal.pone.0244026 – ident: 12611_CR39 doi: 10.25080/TCWV9851 – volume: 1 year: 2011 ident: 12611_CR18 publication-title: AIP Adv. doi: 10.1063/1.3672009 – volume: 26 start-page: 1 year: 2021 ident: 12611_CR27 publication-title: J. Exp. Algorithmics (JEA) doi: 10.1145/3459606 – volume: 31 start-page: 3744 year: 2009 ident: 12611_CR14 publication-title: SIAM J. Sci. Comput. doi: 10.1137/090748330 – volume: 1 start-page: 1 year: 2021 ident: 12611_CR42 publication-title: Quant. Sci. Technol. – volume: 15 start-page: 1 year: 1961 ident: 12611_CR2 publication-title: Am. J. Psychol. – volume: 219 start-page: 343 year: 1994 ident: 12611_CR23 publication-title: Chem. Phys. Lett. doi: 10.1016/0009-2614(94)00117-0 – volume: 83 year: 2020 ident: 12611_CR35 publication-title: Rep. Prog. Phys. doi: 10.1088/1361-6633/ab85b8 – volume: 6 start-page: 164 year: 1927 ident: 12611_CR8 publication-title: J. Math. Phys. doi: 10.1002/sapm192761164 – ident: 12611_CR43 – volume: 51 start-page: 455 year: 2009 ident: 12611_CR6 publication-title: SIAM Rev. doi: 10.1137/07070111X – ident: 12611_CR24 doi: 10.1145/3149526.3149531 – ident: 12611_CR41 – volume: 35 start-page: 551 year: 1993 ident: 12611_CR3 publication-title: SIAM Rev. doi: 10.1137/1035134 – volume: 31 start-page: 279 year: 1966 ident: 12611_CR7 publication-title: Psychometrika doi: 10.1007/BF02289464 – volume: 13 year: 2018 ident: 12611_CR29 publication-title: PLoS ONE doi: 10.1371/journal.pone.0206653 – volume: 1 start-page: 221 year: 1971 ident: 12611_CR12 publication-title: Combin. Math. Appl. – volume: 2 start-page: 1 year: 2014 ident: 12611_CR28 publication-title: Front. Phys. doi: 10.3389/fphy.2014.00005 – volume: 9 start-page: 90 year: 2007 ident: 12611_CR37 publication-title: Comput. Sci. Eng. doi: 10.1109/MCSE.2007.55 – volume: 33 start-page: 2295 year: 2011 ident: 12611_CR15 publication-title: SIAM J. Sci. Comput. doi: 10.1137/090752286 – ident: 12611_CR33 doi: 10.5281/zenodo.4876527 – volume: 90 year: 2018 ident: 12611_CR34 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.90.015002 |
| SSID | ssj0000529419 |
| Score | 2.4744225 |
| Snippet | Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In... Abstract Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard... |
| SourceID | doaj pubmedcentral osti proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 8539 |
| SubjectTerms | 639/705/117 639/705/794 639/766/483/2802 639/766/483/481 Algorithms Boolean Computer applications Computer science D-Wave Decomposition Humanities and Social Sciences MATHEMATICS AND COMPUTING multidisciplinary Parallel quantum annealing Quantum annealing Quantum information Qubits Science Science (multidisciplinary) Software Tensor networks Tensor train Tucker Tucker decomposition |
| SummonAdditionalLinks | – databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR3JbtUw0IICEhd2aGhBQeIGURM78XJCFFFxgKpIIPVmOV7aSq9JecmrxN8z4_ileiy9cIoS29HYs9ozniHkNQ-8EUy0BW8UbFBE2xbGSFV4rqxjIA65j1VLPovDQ3l8rI7SgduQwirXMjEKatdbPCPfA0UiSgzK4e8ufhRYNQq9q6mExk1yCyybCkO6vtCj-YwFvVh1pdJdmZLJvQH0Fd4po1jRg1dVoTb0UUzbD48e2OtvJuefkZO_uU-jVjq4_7_zeUDuJXs0fz8R0ENyw3ePyJ2pQuXPx0R-XcHSr85zA_LY4NX13CxO4D_j6fmQg8Gb7_f9wpsux0h4eO2msPLhCfl-8PHbh09FKrZQ2IaLsWilocxSL61RzMo61L7mhiuuQttKKgKo9hBg8yiEk4a70rGqYjVoQGkcax17Sra6vvPbJAfx6ZWzjAP-a8WNQV9tgI2ZE96ylmWkWi-5tikTORbEWOjoEWdST2jSgCYd0aRVRt7MYy6mPBzX9t5HTM49MYd2_NAvT3RiSS0YkC9jgVbO1MGUyqnSBi68oqaRAsDcQTrQYIpgPl2LgUd21LGGF-MZ2V3jVSe2H_QVUjPyam4GhkUvjOl8v5r6YAgnLTPybKKmGU4w_hSIUBgtNuhsYyKbLd3ZaUwKrsAEKRkA_XZNkVdg_Xuhnl8_ix1ylyKvlA1I012yNS5X_gW5bS_Hs2H5MjLbL32ALx0 priority: 102 providerName: ProQuest |
| Title | Quantum annealing algorithms for Boolean tensor networks |
| URI | https://link.springer.com/article/10.1038/s41598-022-12611-9 https://www.ncbi.nlm.nih.gov/pubmed/35595786 https://www.proquest.com/docview/2667090886 https://www.proquest.com/docview/2667788720 https://www.osti.gov/biblio/1869036 https://pubmed.ncbi.nlm.nih.gov/PMC9123033 https://doaj.org/article/73ecc33f21da4fa09d90cf67e92a5873 |
| Volume | 12 |
| WOSCitedRecordID | wos000798349300029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Jb9QwFLagBYkLYie0jILEDaImduLlyKBWINFRQCANJ8vx0o40TdAkU6n_nmc7M3RYL1wcJbYV-_ltlp-_h9BL6mjFCGsyWgnYoLCmyZTiIrNUaENAHVIbspZ8YLMZn89FfS3Vl48Ji_DAkXBHjMBPCHG4MKp0KhdG5NpRZgVWFWcB5zNn4tpmKqJ6Y1EWYrwlkxN-1IOl8rfJsM_lQYsiEzuWKAD2w6MDwfqds_lrzORPB6fBHp3cQ3dHRzJ9EydwH92w7QN0O6aWvHqI-Mc10Gx9kSpQpMrfOU_V8qxbLYbziz4FTzWddt3Sqjb1Iezw2sZ48P4R-nJy_Pntu2zMkpDpirIha7jCRGPLtRJE89KVtqSKCipc03DMHNhk52DXx5jhiprckKIgJZgurgxpDHmM9tqutU9RCnrPCqMJhYUrBVXKH7I62FEZZjVpSIKKDcWkHiHEfSaLpQxH2YTLSGUJVJaBylIk6NW2z7cIoPHX1lO_ENuWHvw6fACWkCNLyH-xRIIO_DJK8CE8EK72EUN6kCH5FqEJOtysrhzltZfgprDch3xB9YttNUiaPz5Rre3WsY2PvcR5gp5EZtiOE7w2AboPerMdNtmZyG5NuzgPaN4CfIecwKBfbxjqx7D-TKhn_4NQB-gO9gKRV6AsD9HesFrb5-iWvhwW_WqCbrI5CyWfoP3p8az-NAlSBuUprn3JoNyv35_WX78D2RUoeA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoIL78fSAkGCE0RN7KwfB4QoULXqUhWpSL25ju20lbZJ2eyC-qf4jczksdXy6K0HTqts7GiSfPNwZjwfwEtRiKHkMo_FUOMCReZ5bK3ScRDaeY7mUISGtWQkd3bU_r7eXYKf_V4YKqvsbWJjqH3l6Bv5GjoSmVBRjnh3-i0m1ijKrvYUGi0stsPZD1yy1W-3PuL7fcXYxqe9D5txxyoQu6GQ0zhXlnHHgnJWc6eyIguZsEILXeS5YrJAH1YUuEqS0isrfOJ5mvIMTb2ynuee43WvwNWMOotRqSDbnX_ToaxZlupubw5OWKvRP9IeNkYMIiJNY73g_xqaAPypUJ3_FuL-Wan5W7q28YIbt_-353cHbnXxdvS-VZC7sBTKe3C9ZeA8uw_qywyhNTuJLPobS1vzIzs-RLmnRyd1hAF9tF5V42DLiCr98bBsy-brB_D1UsR-CMtlVYbHEKF7CNo7LhDfmRbWUi66wIWnl8HxnA8g7V-xcV2ndSL8GJsm48-VaWFhEBamgYXRA3g9n3Pa9hm5cPQ6IWc-knqEN39Uk0PTmRwjOaon5wVLvc0Km2ivE1cIGTSzQyVRzBXCncFQi_oFOyqsclPTcJRxMYDVHkemM2u1OQfRAF7MT6NBoiyTLUM1a8dQiSpLBvCoRe9cTgxuNboInC0XcL1wI4tnyuOjpum5xhAr4Sj0m14DzsX694N6cvFdPIcbm3ufR2a0tbO9AjcZ6WkyRM-xCsvTySw8hWvu-_S4njxrFD2Cg8vWjF8ZqYvt |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qKSAu7EtoASPBCazYM84sB4QopSJqiYIEUjkN41naSqld4gTUv8av442XVGHprQdOlu0Z69n-3jLzNoBnzLMhpzyP2VDiAoXneay1kLFj0liK4pC5umvJHh-Pxf6-nKzBzy4XJoRVdjKxFtS2NGGPfICKhCchKIcNfBsWMdneeX3yLQ4dpIKntWun0UBk153-wOVb9Wq0jf_6OSE77z69fR-3HQZiM2R8HudCE2qIE0ZLakTmM5cxzSSTPs8F4R71mfe4YuLcCs1sYmma0gzFvtCW5pbicy_BOprkGenB-mT0YfJlucMTfGhZKttMHZwyqFBbhow2EvqJsDSN5Yo2rJsG4KFE5v6bwftn3OZvzttaJ-7c-J-_5k243lri0ZuGdW7Bmituw5WmN-fpHRAfFwi6xXGkURPpkLQf6ekB0j0_PK4iNPWjrbKcOl1EIQcAT4smoL66C58vhOx70CvKwj2ACBWHk9ZQhsjPJNM6eKk9Lkktd4bmtA9p97uVaWuwh1YgU1XHAlChGogohIiqIaJkH14s55w0FUjOHb0VULQcGaqH1xfK2YFqhZHiFBmXUk9SqzOvE2llYjzjThI9FBzJ3AgYVGiEhUrCJoRcmbmqu5dR1ofNDlOqFXiVOgNUH54ub6OoCv4nXbhy0YwJwask6cP9BslLOtHslag8cDZfwfjKi6zeKY4O63LoEo2vhCLRLztuOCPr3x_q4flv8QSuIkOovdF4dwOukcCyyRBVyib05rOFewSXzff5UTV73HJ9BF8vmjV-AX2mljY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Quantum+annealing+algorithms+for+Boolean+tensor+networks&rft.jtitle=Scientific+reports&rft.au=Pelofske%2C+Elijah&rft.au=Hahn%2C+Georg&rft.au=O%E2%80%99Malley%2C+Daniel&rft.au=Djidjev%2C+Hristo+N.&rft.date=2022-05-20&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=12&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-022-12611-9&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41598_022_12611_9 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |