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

TL;DR

This paper introduces a new 'time-to-target' metric for evaluating quantum annealers, which overcomes computational and noise-related limitations of previous success rate metrics, and demonstrates the D-Wave 2X system's competitive performance.

Contribution

The paper proposes a novel evaluation metric for quantum annealers that avoids exponential computation and noise issues, providing a more practical comparison with classical solvers.

Findings

D-Wave 2X performs well on several problem classes.

The 'time-to-target' metric effectively evaluates quantum annealers.

Classical solvers are outperformed on some inputs within short time frames.

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.