On the emerging potential of quantum annealing hardware for combinatorial optimization

• Published in: Journal of heuristics Vol. 30; no. 5-6; pp. 325 - 358
• Main Authors: Tasseff, Byron, Albash, Tameem, Morrell, Zachary, Vuffray, Marc, Lokhov, Andrey Y., Misra, Sidhant, Coffrin, Carleton
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2024; Springer Nature B.V; Springer
• Subjects: 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
• DOI: 10.1007/s10732-024-09530-5

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.