On the emerging potential of quantum annealing hardware for combinatorial optimization
Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art opt...
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| Vydané v: | Journal of heuristics Ročník 30; číslo 5-6; s. 325 - 358 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.12.2024
Springer Nature B.V Springer |
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| ISSN: | 1381-1231, 1572-9397 |
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| Abstract | Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’
Advantage Performance Update
computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work
does not
present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future. |
|---|---|
| AbstractList | Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’
Advantage Performance Update
computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work
does not
present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future. Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’ Advantage Performance Update computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work does not present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future. |
| Author | Lokhov, Andrey Y. Morrell, Zachary Vuffray, Marc Albash, Tameem Coffrin, Carleton Misra, Sidhant Tasseff, Byron |
| Author_xml | – sequence: 1 givenname: Byron surname: Tasseff fullname: Tasseff, Byron organization: Los Alamos National Laboratory – sequence: 2 givenname: Tameem surname: Albash fullname: Albash, Tameem organization: University of New Mexico – sequence: 3 givenname: Zachary surname: Morrell fullname: Morrell, Zachary organization: Los Alamos National Laboratory – sequence: 4 givenname: Marc surname: Vuffray fullname: Vuffray, Marc organization: Los Alamos National Laboratory – sequence: 5 givenname: Andrey Y. surname: Lokhov fullname: Lokhov, Andrey Y. organization: Los Alamos National Laboratory – sequence: 6 givenname: Sidhant surname: Misra fullname: Misra, Sidhant organization: Los Alamos National Laboratory – sequence: 7 givenname: Carleton orcidid: 0000-0003-3238-1699 surname: Coffrin fullname: Coffrin, Carleton email: cjc@lanl.gov organization: Los Alamos National Laboratory |
| BackLink | https://www.osti.gov/biblio/2426409$$D View this record in Osti.gov |
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| SubjectTerms | Annealing Artificial Intelligence Benchmarks Calculus of Variations and Optimal Control; Optimization Combinatorial analysis Hardware KNOWLEDGE MANAGEMENT AND PRESERVATION Management Science Mathematics MATHEMATICS AND COMPUTING Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Performance assessment State-of-the-art reviews |
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