Garden optimization problems for benchmarking quantum annealers

Abstract

We benchmark the 5000+ qubit system 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 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 sometimes produce more favourable results if they can solve the problems.

Benchmarking quantum annealing with maximum cardinality matching problems

“…Obviously, the energy gap between the ground state and the excited state is much smaller for the Advantage_system4.1 annealing schedule, which means the G 1 problem is then harder to solve, resulting in the longer annealing times required to achieve the same success probabilities as with the other annealing schedules. This might be different for other problems but especially for small and/or sparse problems, it was observed that DW_2000Q processors could achieve a better performance than Advantage processors (Calaza et al, 2021;Willsch et al, 2022a).…”

“…Previous studies compared D-Wave 2000Q and Advantage system (Calaza et al, 2021;McLeod and Sasdelli, 2022;Willsch et al, 2022a), D-Wave 2000Q, Advantage system and Advantage2 prototype (Pelofske, 2023), and D-Wave Two, D-Wave 2X, D-Wave 2000Q, and Advantage system (Pokharel et al, 2021). (iii) Another aspect is the search for quantum speedup (Rønnow et al, 2014) and investigations of the performance of quantum processors in comparison to classical algorithms.…”

Integration of Simulated Quantum Annealing in Parallel Tempering and Population Annealing for Heterogeneous-Profile QUBO Exploration

“…An important category of optimization problems is optimal placement. In this context, an interesting real-world application is garden optimization [61], whose target is to optimally place n plants in n pots (as shown in Figure 5d). This problem can be written in QUBO formulation with n 2 binary variables, one for each plant-pot pair.…”

Quantum Annealing for Jet Clustering with Thrust

“…A reasonable starting point for optimizing the chain strength is to set it to the largest coupling strength of the original QUBO problem [38]. We define the relative chain strength (RCS) as…”