Benchmarking a quantum annealing processor with the time-to-target metric

TLDR

Metrics based on ground‑state success rates for quantum annealers are computationally prohibitive and highly sensitive to analog noise, limiting their usefulness. The study introduces a novel “time‑to‑target” metric to evaluate quantum annealers. This metric challenges software solvers to match quantum‑annealer results within a short time, avoiding computational and noise issues. Evaluation of the D‑Wave 2X on various problem classes shows it performs well relative to state‑of‑the‑art single‑threaded CPU solvers.

Abstract

In the evaluation of quantum annealers, metrics based on ground state success rates have two major drawbacks. First, evaluation requires computation time for both quantum and classical processors that grows exponentially with problem size. This makes evaluation itself computationally prohibitive. Second, results are heavily dependent on the effects of analog noise on the quantum processors, which is an engineering issue that complicates the study of the underlying quantum annealing algorithm. We introduce a novel "time-to-target" metric which avoids these two issues by challenging software solvers to match the results obtained by a quantum annealer in a short amount of time. We evaluate D-Wave's latest quantum annealer, the D-Wave 2X system, on an array of problem classes and find that it performs well on several input classes relative to state of the art software solvers running single-threaded on a CPU.