Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases

TL;DR

This paper benchmarks quantum and quantum-inspired annealers on practical combinatorial optimization problems, revealing their current limitations in scalability and practical application, and discusses future research directions.

Contribution

It provides a comparative analysis of quantum and quantum-inspired annealers on real-world problems and evaluates the effectiveness of decomposition methods for scalability.

Findings

Both annealers perform well on small, simple problems.

Performance degrades on larger, practical problems.

Decomposition extends scalability but remains insufficient for practical use.

Abstract

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.