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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Vydáno v:Journal of heuristics Ročník 30; číslo 5-6; s. 325 - 358
Hlavní autoři: Tasseff, Byron, Albash, Tameem, Morrell, Zachary, Vuffray, Marc, Lokhov, Andrey Y., Misra, Sidhant, Coffrin, Carleton
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2024
Springer Nature B.V
Springer
Témata:
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
Quantum annealing
Ising
Optimization
Quadratic
QUBO
ISSN:1381-1231, 1572-9397
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Popis
Shrnutí: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.
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National Science Foundation (NSF)
89233218CNA000001; 2037755; 20210114ER
LA-UR-22-29705
USDOE Laboratory Directed Research and Development (LDRD) Program
USDOE National Nuclear Security Administration (NNSA)
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-024-09530-5