A Performance Estimator for Quantum Annealers: Gauge selection and Parameter Setting

TL;DR

This paper introduces a performance estimator for quantum annealers that guides gauge selection and parameter setting, aiming to improve problem-solving efficiency on large-scale devices.

Contribution

It proposes a score function that requires fewer readouts to evaluate performance, aiding in optimal gauge and parameter selection for quantum annealing.

Findings

Effective gauge selection using the score function

Parameter setting guidance for embedding strength

Potential to reduce benchmark and solution times

Abstract

With the advent of large-scale quantum annealing devices, several challenges have emerged. For example, it has been shown that the performance of a device can be significantly affected by several degrees of freedom when programming the device; a common example being gauge selection. To date, no experimentally-tested strategy exists to select the best programming specifications. We developed a score function that can be calculated from a number of readouts much smaller than the number of readouts required to find the desired solution. We show how this performance estimator can be used to guide, for example, the selection of the optimal gauges out of a pool of random gauge candidates and how to select the values of parameters for which we have no a priori knowledge of the optimal value. For the latter, we illustrate the concept by applying the score function to set the strength of the parameter intended to enforce the embedding of the logical graph into the hardware architecture, a challenge frequently encountered in the implementation of real-world problem instances. Since the harder the problem instances, the more useful the strategies proposed in this work are, we expect the programming strategies proposed to significantly reduce the time of future benchmark studies and in help finding the solution of hard-to-solve real-world applications implemented in the next generation of quantum annealing devices.