Garden optimization problems for benchmarking quantum annealers

TL;DR

This paper evaluates the performance of D-Wave's Advantage quantum annealer and hybrid solver on a new class of scalable optimization problems called garden problems, highlighting their capabilities and limitations.

Contribution

It introduces garden optimization problems, formulates them as QUBO, and benchmarks their solution performance on D-Wave's latest quantum hardware and hybrid solver.

Findings

Advantage system solves larger problems faster than predecessors.

Hybrid solver outperforms older systems on large problems.

Older DW2000Q sometimes yields better results on smaller problems.

Abstract

We benchmark the 5000+ qubit system Advantage coupled with the Hybrid Solver Service 2 released by D-Wave Systems Inc. in September 2020 by using a new class of optimization problems called garden optimization problems known in companion planting. These problems are scalable to an arbitrarily large number of variables and intuitively find application in real-world scenarios. We derive their QUBO formulation and illustrate their relation to the quadratic assignment problem. We demonstrate that the Advantage system and the new hybrid solver can solve larger problems in less time than their predecessors. However, we also show that the solvers based on the 2000+ qubit system DW2000Q sometimes produce more favourable results if they can solve the problems.