On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization

TL;DR

This paper evaluates the latest quantum annealing hardware's performance on combinatorial optimization problems, showing potential benefits in specific contrived cases but not yet a definitive advantage over classical methods.

Contribution

It provides a benchmarking assessment of D-Wave's latest quantum annealer, highlighting instances where it outperforms classical algorithms in certain problem classes.

Findings

Quantum annealer shows run time benefits on some contrived problems.

Hardware continues to improve with each iteration, warranting further benchmarking.

No conclusive evidence yet of universal performance superiority over classical methods.

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' most recent 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.