Diversity metric for evaluation of quantum annealing

• URL: http://arxiv.org/abs/2110.10196v2
• Date: Sat, 23 Oct 2021 00:25:55 GMT
• Title: Diversity metric for evaluation of quantum annealing
• Authors: Alex Zucca, Hossein Sadeghi, Masoud Mohseni, and Mohammad H. Amin
• Abstract summary: It is not known how well quantum solvers sample the configuration space in comparison to their classical counterparts. We use time-to-diversity as a metric for evaluation of meta-heuristics solvers. This suggests that a portfolio solver that combines quantum and classical solutions may win over all solvers.
• Score: 1.0499611180329802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving discrete NP-hard problems is an important part of scientific discoveries and operations research as well as many commercial applications. A commonly used metric to compare meta-heuristic solvers is the time required to obtain an optimal solution, known as time to solution. However, for some applications it is desirable to have a set of high-quality and diverse solutions, instead of a single optimal one. For these applications, time to solution may not be informative of the performance of a solver, and another metric would be necessary. In particular, it is not known how well quantum solvers sample the configuration space in comparison to their classical counterparts. Here, we apply a recently introduced collective distance measure in solution space to quantify diversity by Mohseni et. al. and, based on that, we employ time-to-diversity as a metric for evaluation of meta-heuristics solvers. We use this measure to compare the performance of the D-Wave quantum annealing processor with a few classical heuristic solvers on a set of synthetic problems and show that D-Wave quantum annealing processor is indeed a competitive heuristic, and on many instances outperforms state-of-the-art classical solvers, while it remains on par on other instances. This suggests that a portfolio solver that combines quantum and classical solutions may win over all solvers.

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